Abstract In the agricultural industry, the weed and crop identification and classification are major technical and economical importance. Two classification algorithms are focused in this paper. And the better classification algorithm has been selected to classify weed and crop from the images. There are three main parts of proposed system are segmentation, classification and error calculation. The developed algorithm based on area thresholding has been tested on weeds and various locations. Forty one sample images have been tested and the result of some weed coverage rate is illustrated. Moreover, the misclassification rate is also computed. An algorithm has been done to automate the tasks of segmentation and classification. The overall process is implemented in MATLAB. Keywords - Objects segmentation, Image processing, Plant classification, Area Thresholding
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
WECSPE texture feature obtaining high degree of success rate in classification.
IRJET- Applications of different Techniques in Agricultural System: A ReviewIRJET Journal
This document reviews different techniques used in agricultural systems, including for identifying plant diseases and providing fertilizer recommendations. It discusses several proposed systems: 1) A machine learning approach using support vector machines to identify and classify five cotton plant diseases from images and provide treatment recommendations via an Android app. 2) An automatic plant disease detection system using image processing and Gaussian smoothing to identify affected plant spots, achieving 90.96% accuracy. 3) An automated system to mix fertilizers in required ratios and supply them with irrigation water to maintain optimal soil moisture levels and increase crop yields. 4) A remote monitoring and control system using sensors to control fertilizer injection and irrigation based on soil moisture levels. 5) An intelligent drip irrigation and fertigation system
Pest Control in Agricultural Plantations Using Image ProcessingIOSR Journals
Abstract: Monocropped plantations are unique to India and a handful of countries throughout the globe. Essentially, the FOREST approach of growing coffee along with in India has enabled the plantation to fight many outbreaks of pests and diseases. Mono cropped Plantations are under constant threat of pest and disease incidence because it favours the build up of pest population. To cope with these problems, an automatic pest detection algorithm using image processing techniques in MATLAB has been proposed in this paper. Image acquisition devices are used to acquire images of plantations at regular intervals. These images are then subjected to pre-processing, transformation and clustering.
This document summarizes an article that proposes using image processing techniques in agriculture to detect weed areas in crop fields. The researchers took images from agricultural fields and used MATLAB to implement image segmentation algorithms to identify weed areas. The article provides background on how image processing can be used for various agricultural applications like detecting diseased plants, quantifying affected areas, and determining fruit size and shape. It also reviews different existing image classification techniques used for agricultural disease detection, such as neural networks, support vector machines, and others.
The document summarizes a study that mapped forest types and plantation works in Bidar Forest Division, Karnataka, India using remote sensing and GIS techniques. Satellite imagery was analyzed which revealed that 84% of the area is covered by crop land, 1.778% by dry deciduous forest, and 1.38% by mixed plantation. Maps were generated showing forest type distribution and plantation works during 2011-2012 and planned for 2012-2013. Around 12 square kilometers were planted in 2011-2012, with most in Humnbad range and least in Aurad range. Proper forest management can be achieved using geoinformatics technologies.
IRJET- Leaf Disease Detection using Image ProcessingIRJET Journal
This document discusses a system for detecting leaf diseases in plants using image processing techniques. It involves taking images of plant leaves, pre-processing the images by removing noise and enhancing contrast, segmenting the images to identify diseased regions, extracting features like color and texture, and classifying the diseases. The system uses techniques like k-means clustering, color analysis, and neural networks. It aims to help farmers identify diseases early to control them. A proposed smart agriculture system would monitor soil moisture and temperature using sensors and detect diseases using these image processing methods to advise farmers remotely.
This document presents a study on using color texture feature analysis to detect surface defects on pomegranates. The researchers developed a method involving cropping images of pomegranates, converting them to HSI color space, generating SGDM matrices to extract 18 texture features for each image, and using support vector machines (SVM) classification to identify the best features for detecting infections. The optimal features identified were cluster shade, product moment, and mean intensity, achieving classification accuracy of 99.88%, 99.88%, and 99.81% respectively.
Analysis And Detection of Infected Fruit Part Using Improved k-means Clusteri...IJSRD
Drastic increase in the overseas commerce has increased nowadays .Modern food industries work on the quality and safety of the products. Fruits such as oranges and apple are imported and exported on large scale. Identifying the defect manually become time consuming process. The combined study of image processing and clustering technique gave a turning point to the defect defection in fruits. This paper gives a solution for defect detection and classification of fruits using improved K-means clustering algorithm. Based on their color pixels are clustered. Then the merging takes place to a specific no of regions. Although defect segmentation is not depend on the color, it causes to produce different power to different regions of image. We have taken some of the fruits for the experimental results to clarify the proposed approach to improve the analysis and detection of fruit quality to minimize the precious and computational time. The proposed system is effective due to result obtained.
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
WECSPE texture feature obtaining high degree of success rate in classification.
IRJET- Applications of different Techniques in Agricultural System: A ReviewIRJET Journal
This document reviews different techniques used in agricultural systems, including for identifying plant diseases and providing fertilizer recommendations. It discusses several proposed systems: 1) A machine learning approach using support vector machines to identify and classify five cotton plant diseases from images and provide treatment recommendations via an Android app. 2) An automatic plant disease detection system using image processing and Gaussian smoothing to identify affected plant spots, achieving 90.96% accuracy. 3) An automated system to mix fertilizers in required ratios and supply them with irrigation water to maintain optimal soil moisture levels and increase crop yields. 4) A remote monitoring and control system using sensors to control fertilizer injection and irrigation based on soil moisture levels. 5) An intelligent drip irrigation and fertigation system
Pest Control in Agricultural Plantations Using Image ProcessingIOSR Journals
Abstract: Monocropped plantations are unique to India and a handful of countries throughout the globe. Essentially, the FOREST approach of growing coffee along with in India has enabled the plantation to fight many outbreaks of pests and diseases. Mono cropped Plantations are under constant threat of pest and disease incidence because it favours the build up of pest population. To cope with these problems, an automatic pest detection algorithm using image processing techniques in MATLAB has been proposed in this paper. Image acquisition devices are used to acquire images of plantations at regular intervals. These images are then subjected to pre-processing, transformation and clustering.
This document summarizes an article that proposes using image processing techniques in agriculture to detect weed areas in crop fields. The researchers took images from agricultural fields and used MATLAB to implement image segmentation algorithms to identify weed areas. The article provides background on how image processing can be used for various agricultural applications like detecting diseased plants, quantifying affected areas, and determining fruit size and shape. It also reviews different existing image classification techniques used for agricultural disease detection, such as neural networks, support vector machines, and others.
The document summarizes a study that mapped forest types and plantation works in Bidar Forest Division, Karnataka, India using remote sensing and GIS techniques. Satellite imagery was analyzed which revealed that 84% of the area is covered by crop land, 1.778% by dry deciduous forest, and 1.38% by mixed plantation. Maps were generated showing forest type distribution and plantation works during 2011-2012 and planned for 2012-2013. Around 12 square kilometers were planted in 2011-2012, with most in Humnbad range and least in Aurad range. Proper forest management can be achieved using geoinformatics technologies.
IRJET- Leaf Disease Detection using Image ProcessingIRJET Journal
This document discusses a system for detecting leaf diseases in plants using image processing techniques. It involves taking images of plant leaves, pre-processing the images by removing noise and enhancing contrast, segmenting the images to identify diseased regions, extracting features like color and texture, and classifying the diseases. The system uses techniques like k-means clustering, color analysis, and neural networks. It aims to help farmers identify diseases early to control them. A proposed smart agriculture system would monitor soil moisture and temperature using sensors and detect diseases using these image processing methods to advise farmers remotely.
This document presents a study on using color texture feature analysis to detect surface defects on pomegranates. The researchers developed a method involving cropping images of pomegranates, converting them to HSI color space, generating SGDM matrices to extract 18 texture features for each image, and using support vector machines (SVM) classification to identify the best features for detecting infections. The optimal features identified were cluster shade, product moment, and mean intensity, achieving classification accuracy of 99.88%, 99.88%, and 99.81% respectively.
Analysis And Detection of Infected Fruit Part Using Improved k-means Clusteri...IJSRD
Drastic increase in the overseas commerce has increased nowadays .Modern food industries work on the quality and safety of the products. Fruits such as oranges and apple are imported and exported on large scale. Identifying the defect manually become time consuming process. The combined study of image processing and clustering technique gave a turning point to the defect defection in fruits. This paper gives a solution for defect detection and classification of fruits using improved K-means clustering algorithm. Based on their color pixels are clustered. Then the merging takes place to a specific no of regions. Although defect segmentation is not depend on the color, it causes to produce different power to different regions of image. We have taken some of the fruits for the experimental results to clarify the proposed approach to improve the analysis and detection of fruit quality to minimize the precious and computational time. The proposed system is effective due to result obtained.
Identification and Classification of Leaf Diseases in Turmeric PlantsIJERA Editor
Plant disease identification is the most important sector in agriculture. Turmeric is one of the important
rhizomatous crops grown in India. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot,
and leaf blotch. The identification of plant diseases requires close monitoring and hence this paper adopts
technologies to manage turmeric plant diseases caused by fungi to enable production of high quality crop yields.
Various image processing and machine learning techniques are used to identify and classify the diseases in
turmeric leaf. The dataset with 800 leaf images of different categories were pre-processed and segmented to
promote efficient feature extraction. Machine learning algorithms like support vector machine, decision tree and
naïve bayes were applied to train the model. The performance of the model was evaluated using 10 fold cross
validation and the results are reported.
This document summarizes a research paper on developing a real-time system for identifying crop diseases, pest damage, and nutrient deficiencies using image processing. The proposed system uses a camera to capture images of plant leaves which are then analyzed using MATLAB software. Machine learning algorithms like K-means clustering and support vector machines are used to analyze images, extract features, and classify diseases. If a disease is identified, the system will automatically sprinkle the appropriate fertilizers. The goal is to help farmers more easily and accurately monitor crop health without requiring constant supervision or expert knowledge, thereby improving yields.
Classification of Macronutrient Deficiencies in Maize Plant Using Machine Lea...IJECEIAES
This paper aims to classify macronutrient deficiencies in maize plants using machine learning techniques. Two feature extraction methods are used to generate two feature sets from images of healthy and deficient maize leaves. Various machine learning classifiers including artificial neural networks, support vector machines, k-nearest neighbors, and deep networks with autoencoders are applied to the two feature sets and their classification accuracies are compared. The results show that deep networks with autoencoders achieved the highest accuracy of 100% for one feature set, while k-nearest neighbors performed best for the other feature set. This study demonstrates the effectiveness of machine learning approaches for nutrient deficiency classification in plants.
This document presents a novel approach for detecting and classifying plant leaf diseases using image processing techniques. The proposed framework includes image preprocessing, segmentation of diseased areas using k-means clustering, feature extraction of texture features using GLCM, and classification of diseases using support vector machine (SVM) and decision tree classifiers. The framework is tested on citrus leaf images to detect diseases. Future work could improve classification accuracy and apply the method to other plant species.
Simulink model for automatic detection and counting of the number of white fl...eSAT Journals
Abstract
The whitefly is a small white insect which feeds from the sap of different variety of plants and causes wide spread destruction. It is
one of the most harmful pests for crops like tomato, cabbage, broccoli etc. The small size of the pest, which is around 2mm in
length, poses a great challenge to detect these pests using image processing technique in the presence of noise, which could be in
the forms of leaf veins, trichomes, water droplets and dust etc. Early detection of pests is required to reduce or prevent the
damage caused by the white-flies. It is also important to count the number of pests as accurately as possible, because based on the
number of pests the amount of pesticide can be determined or appropriate advice can be given to the persons concerned. Manual
counting of white-flies is very tedious and error prone if the number of pests is very high. We can do the counting satisfactorily by
using efficient image processing methods. In this paper a method is proposed to detect and count the number of white-flies using
image processing on Simulink and Matlab software.
Keywords:Image Processing, Simulink, whitefly, count
Image processing to identify foliar infectionTHANMAY JS
Most important and basic recognition requirement for measuring percentage of Leaf Infection was expressed by “Stoyer’s experiment to determine Foliar Infections”. Other than this tedious and destructive method, a new and simple method for plant leaf infection measurement using image processing technique is provided in this research work. Leaves with different shape and size of same plant were selected for study. Conventional Experiments were performed to test the performance of the estimating system by comparing the estimated leaf infected area. The ranges of relative errors of estimation were analyzed for better future of research and development in this area. The results illuminated more accurate of the estimating leaf infected area. So it can be concluded that the method developed in this study obtained the sufficient accuracy for the possible non-destructive leaf Infection area measurement.
Image Mining for Flower Classification by Genetic Association Rule Mining Usi...IJAEMSJORNAL
Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules.
Computer Vision based Model for Fruit Sorting using K-Nearest Neighbour clas...IJEEE
This document presents a computer vision based model for fruit sorting using a K-nearest neighbor classifier. It uses color and morphological features extracted from images to classify six types of fruits (red apples, green apples, golden apples, oranges, bananas, and carrots). The methodology involves image segmentation using K-means clustering, followed by extraction of color features from RGB and HSI color spaces and morphological features. A K-nearest neighbor classifier with Euclidean distance metric is then used for classification. The system achieved 100% accuracy in classifying the six fruit types based on the extracted features.
Classification of Mango Fruit Varieties using Naive Bayes Algorithmijtsrd
Mangos are an important agricultural commodity in the global market for fresh products. In Myanmar, the type of mango called SeinTaLone is the best taste and the most people like it. Another type of mango called MaSawYin is not good taste but it is visually similar to the SeinTaLone. So, some people are difficult to classify the mango varieties. A means for distinguishing mango varieties is needed and therefore, some reliable technique is needed to discriminate varieties rapidly and non destructively. The main objective of this research was to classify the varieties of mango fruit that occur in Myanmar using Naive Bayes algorithm. The methodology involved image acquisition, pre processing and segmentation, feature extraction and classification of mango varieties. A method for classifying varieties of mangos using image processing technique is proposed in this paper. RGB image was first converted to HSV image. Then by using edge detection method and morphological operation, region of interest was segmented by taking into account only the HUE component image of the HSV image. Later, a total of 4 shape features and 13 texture features were extracted. Extracted features were given as inputs to a Naive Byaesian classifier to classify the test images as each type. The data set used had 50 mango images for each varieties of mango for training and 20 images of mango for each variety for testing. Ohnmar Win "Classification of Mango Fruit Varieties using Naive Bayes Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26677.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26677/classification-of-mango-fruit-varieties-using-naive-bayes-algorithm/ohnmar-win
Quality Evaluation Technique For Phyllanthus Emblica(Gooseberry) Using Comput...ijsrd.com
This paper proposes quality assessment method to classify a phyllanthus emblica (gooseberry) using computer vision by surface and geometric features. India is one of the most important gooseberry producers in North Asia, than Germany, Poland, U.K, Russia etc., but fruit sorting in some area is still done by hand which is tedious and inaccurate. Thus, the need exists for improvement of efficiency and accuracy of this fruit quality assessment that can meet the demands of international markets. Low-cost and non-destructive technologies capable of sorting gooseberry according to their properties would help to promote the gooseberry export industries. This paper propose the method of colorization and extracting value parameters, by this parameters the detection of browning or affected part and identification of the uniform shape and size. This differentiates the quality of gooseberries processed as well as fresh. For classification the decision tree is used.
The quality identification of fruits in image processing using matlabeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This paper proposes an automatic method for early pest detection in crops using image processing and machine learning. The method involves taking images of crop leaves and applying preprocessing techniques like color to grayscale conversion, resizing, filtering and segmentation to isolate the pests from the background. Features are then extracted from the images using gray level co-occurrence matrix and regional properties. A support vector machine classifier is trained on the image features to classify images as containing pests or not containing pests, allowing for early detection and prevention of crop damage from pests.
IRJET- Identify Quality Index of the Fruit Vegetable by Non Destructive or wi...IRJET Journal
This document presents a literature review and proposed system for identifying the quality of fruits and vegetables using non-destructive image processing techniques. It discusses using computer vision algorithms like filtering, segmentation, feature extraction and classification to analyze images of fruits and determine quality metrics like size, shape, color and defects. The proposed system would capture images, preprocess them, extract features and classify fruits as good or defective quality without damaging the fruits. This could help automate quality inspection and grading of agricultural produce.
Analysis of Near-infrared (NIR) spectroscopy for chlorophyll prediction in oi...journalBEEI
This document analyzes the use of near-infrared (NIR) spectroscopy to predict chlorophyll content in oil palm leaves as a non-invasive alternative to traditional chemical analysis methods. NIR spectral data from oil palm leaf samples were collected and correlated with chlorophyll readings using partial least squares regression with different preprocessing techniques. Savitzky-Golay smoothing produced the best results with an R^2 of 0.9998 for calibration and prediction, indicating NIR spectroscopy can accurately predict chlorophyll content in oil palm leaves in a time-saving manner without destructive sampling.
Feature extraction of Jabon (Anthocephalus sp) leaf disease using discrete wa...TELKOMNIKA JOURNAL
Jabon (Anthocephalus cadamba (Roxb.) Miq) is one type of forest plants that have very rapid growth until the process of the harvest. One inhibitor is a disease that attacks the leaves in the form of spots and blight that can cause death during the growth process of this tree. The purpose of this process is to detect the object of diseases that attack the leaves of jabon at the time in the nursery. Images of affected jabon leaf disease segmented by reducing the RGB color cylinders to separate the disease object from the background. Reduced channel G-R provides information in the form of disease areas contained in the image of Jabon leaf. Furthermore, the characteristics of leaf disease can be detected well using DWT in the 3-level decomposition process with SVM classification results that can separate both classes of spots and blight by 84.672%.
Herb Leaves Recognition using Gray Level Co-occurrence Matrix and Five Distan...IJECEIAES
This document describes a study on herb leaf recognition using the Gray Level Co-occurrence Matrix (GLCM) method of feature extraction and five distance-based similarity measures. The researchers tested recognition accuracy on 10, 20, and 30 types of herb leaves using GLCM features and the Canberra, Chebyshev, Cityblock, Euclidean, and Minkowski distances. They found the highest accuracy of 92% was achieved using GLCM features and the Canberra distance on 10 leaf images. Accuracy decreased to 50.67% and 60% when using 20 and 30 leaf images, respectively.
IRJET- Automatic Fruit Quality Detection SystemIRJET Journal
This document presents an automatic fruit quality detection system that uses computer vision and image processing techniques. The system captures images of fruits on a conveyor belt using a camera. It then performs image processing on the images to analyze features like color, size, and texture. It can detect defects in fruits based on pixel analysis of the images. The fruits are then sorted based on color and graded based on size. The system aims to automate and improve the efficiency of the fruit sorting and grading process compared to manual methods. It analyzes the images, detects quality factors, and controls hardware like the conveyor belt based on the analysis results.
IRJET- A Fruit Quality Inspection Sytem using Faster Region Convolutional...IRJET Journal
This document presents a fruit quality inspection system using a Faster Region Convolutional Neural Network (RCNN). The system uses a Faster RCNN algorithm and softmax classifier to detect and classify fruit quality. Faster RCNN is capable of performing both region proposal generation and objection tasks using a single convolutional network, making it faster than RCNN. The system feeds images of fruit into a convolutional network to generate feature maps. Region proposals are identified from the feature maps and reshaped to be classified and have bounding boxes predicted, allowing for automated fruit quality inspection.
Abstract In this paper illustrates the improvement of a low cost machine vision system using webcams and image processing algorithms for defect detection and sorting of tomatoes The sorting decision was based on three features extracted by the different image processing algorithms. This methodology based on the color features, which used for detecting the BER from good tomatoes. Two methods were developed for decision based sorting. The color image threshold method with shape factor was found efficient for differentiating good and defective tomatoes. The overall accuracy of defect detection attained was 94 and 96.5% respectively. Comparison of the results is also presented in this paper. Keywords: Dither Image, Stem Image, Histogram, Tomato.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Digital image processing methods for estimating leaf area of cucumber plantsnooriasukmaningtyas
Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.
IRJET- Detection of Plant Leaf Diseases using Image Processing and Soft-C...IRJET Journal
This document presents a method for detecting plant leaf diseases using image processing and soft computing techniques. It involves taking images of plant leaves using a digital camera, pre-processing the images, segmenting the images to identify infected regions, extracting features from the infected regions, and classifying the disease based on the features. The method was tested on various plant leaf image datasets with an accuracy of 63% and was able to identify diseases for tomatoes, corn, grapes, peaches and peppers. The automatic detection technique can help identify diseases at an early stage with less time and effort compared to manual detection methods.
Identification and Classification of Leaf Diseases in Turmeric PlantsIJERA Editor
Plant disease identification is the most important sector in agriculture. Turmeric is one of the important
rhizomatous crops grown in India. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot,
and leaf blotch. The identification of plant diseases requires close monitoring and hence this paper adopts
technologies to manage turmeric plant diseases caused by fungi to enable production of high quality crop yields.
Various image processing and machine learning techniques are used to identify and classify the diseases in
turmeric leaf. The dataset with 800 leaf images of different categories were pre-processed and segmented to
promote efficient feature extraction. Machine learning algorithms like support vector machine, decision tree and
naïve bayes were applied to train the model. The performance of the model was evaluated using 10 fold cross
validation and the results are reported.
This document summarizes a research paper on developing a real-time system for identifying crop diseases, pest damage, and nutrient deficiencies using image processing. The proposed system uses a camera to capture images of plant leaves which are then analyzed using MATLAB software. Machine learning algorithms like K-means clustering and support vector machines are used to analyze images, extract features, and classify diseases. If a disease is identified, the system will automatically sprinkle the appropriate fertilizers. The goal is to help farmers more easily and accurately monitor crop health without requiring constant supervision or expert knowledge, thereby improving yields.
Classification of Macronutrient Deficiencies in Maize Plant Using Machine Lea...IJECEIAES
This paper aims to classify macronutrient deficiencies in maize plants using machine learning techniques. Two feature extraction methods are used to generate two feature sets from images of healthy and deficient maize leaves. Various machine learning classifiers including artificial neural networks, support vector machines, k-nearest neighbors, and deep networks with autoencoders are applied to the two feature sets and their classification accuracies are compared. The results show that deep networks with autoencoders achieved the highest accuracy of 100% for one feature set, while k-nearest neighbors performed best for the other feature set. This study demonstrates the effectiveness of machine learning approaches for nutrient deficiency classification in plants.
This document presents a novel approach for detecting and classifying plant leaf diseases using image processing techniques. The proposed framework includes image preprocessing, segmentation of diseased areas using k-means clustering, feature extraction of texture features using GLCM, and classification of diseases using support vector machine (SVM) and decision tree classifiers. The framework is tested on citrus leaf images to detect diseases. Future work could improve classification accuracy and apply the method to other plant species.
Simulink model for automatic detection and counting of the number of white fl...eSAT Journals
Abstract
The whitefly is a small white insect which feeds from the sap of different variety of plants and causes wide spread destruction. It is
one of the most harmful pests for crops like tomato, cabbage, broccoli etc. The small size of the pest, which is around 2mm in
length, poses a great challenge to detect these pests using image processing technique in the presence of noise, which could be in
the forms of leaf veins, trichomes, water droplets and dust etc. Early detection of pests is required to reduce or prevent the
damage caused by the white-flies. It is also important to count the number of pests as accurately as possible, because based on the
number of pests the amount of pesticide can be determined or appropriate advice can be given to the persons concerned. Manual
counting of white-flies is very tedious and error prone if the number of pests is very high. We can do the counting satisfactorily by
using efficient image processing methods. In this paper a method is proposed to detect and count the number of white-flies using
image processing on Simulink and Matlab software.
Keywords:Image Processing, Simulink, whitefly, count
Image processing to identify foliar infectionTHANMAY JS
Most important and basic recognition requirement for measuring percentage of Leaf Infection was expressed by “Stoyer’s experiment to determine Foliar Infections”. Other than this tedious and destructive method, a new and simple method for plant leaf infection measurement using image processing technique is provided in this research work. Leaves with different shape and size of same plant were selected for study. Conventional Experiments were performed to test the performance of the estimating system by comparing the estimated leaf infected area. The ranges of relative errors of estimation were analyzed for better future of research and development in this area. The results illuminated more accurate of the estimating leaf infected area. So it can be concluded that the method developed in this study obtained the sufficient accuracy for the possible non-destructive leaf Infection area measurement.
Image Mining for Flower Classification by Genetic Association Rule Mining Usi...IJAEMSJORNAL
Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules.
Computer Vision based Model for Fruit Sorting using K-Nearest Neighbour clas...IJEEE
This document presents a computer vision based model for fruit sorting using a K-nearest neighbor classifier. It uses color and morphological features extracted from images to classify six types of fruits (red apples, green apples, golden apples, oranges, bananas, and carrots). The methodology involves image segmentation using K-means clustering, followed by extraction of color features from RGB and HSI color spaces and morphological features. A K-nearest neighbor classifier with Euclidean distance metric is then used for classification. The system achieved 100% accuracy in classifying the six fruit types based on the extracted features.
Classification of Mango Fruit Varieties using Naive Bayes Algorithmijtsrd
Mangos are an important agricultural commodity in the global market for fresh products. In Myanmar, the type of mango called SeinTaLone is the best taste and the most people like it. Another type of mango called MaSawYin is not good taste but it is visually similar to the SeinTaLone. So, some people are difficult to classify the mango varieties. A means for distinguishing mango varieties is needed and therefore, some reliable technique is needed to discriminate varieties rapidly and non destructively. The main objective of this research was to classify the varieties of mango fruit that occur in Myanmar using Naive Bayes algorithm. The methodology involved image acquisition, pre processing and segmentation, feature extraction and classification of mango varieties. A method for classifying varieties of mangos using image processing technique is proposed in this paper. RGB image was first converted to HSV image. Then by using edge detection method and morphological operation, region of interest was segmented by taking into account only the HUE component image of the HSV image. Later, a total of 4 shape features and 13 texture features were extracted. Extracted features were given as inputs to a Naive Byaesian classifier to classify the test images as each type. The data set used had 50 mango images for each varieties of mango for training and 20 images of mango for each variety for testing. Ohnmar Win "Classification of Mango Fruit Varieties using Naive Bayes Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26677.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26677/classification-of-mango-fruit-varieties-using-naive-bayes-algorithm/ohnmar-win
Quality Evaluation Technique For Phyllanthus Emblica(Gooseberry) Using Comput...ijsrd.com
This paper proposes quality assessment method to classify a phyllanthus emblica (gooseberry) using computer vision by surface and geometric features. India is one of the most important gooseberry producers in North Asia, than Germany, Poland, U.K, Russia etc., but fruit sorting in some area is still done by hand which is tedious and inaccurate. Thus, the need exists for improvement of efficiency and accuracy of this fruit quality assessment that can meet the demands of international markets. Low-cost and non-destructive technologies capable of sorting gooseberry according to their properties would help to promote the gooseberry export industries. This paper propose the method of colorization and extracting value parameters, by this parameters the detection of browning or affected part and identification of the uniform shape and size. This differentiates the quality of gooseberries processed as well as fresh. For classification the decision tree is used.
The quality identification of fruits in image processing using matlabeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This paper proposes an automatic method for early pest detection in crops using image processing and machine learning. The method involves taking images of crop leaves and applying preprocessing techniques like color to grayscale conversion, resizing, filtering and segmentation to isolate the pests from the background. Features are then extracted from the images using gray level co-occurrence matrix and regional properties. A support vector machine classifier is trained on the image features to classify images as containing pests or not containing pests, allowing for early detection and prevention of crop damage from pests.
IRJET- Identify Quality Index of the Fruit Vegetable by Non Destructive or wi...IRJET Journal
This document presents a literature review and proposed system for identifying the quality of fruits and vegetables using non-destructive image processing techniques. It discusses using computer vision algorithms like filtering, segmentation, feature extraction and classification to analyze images of fruits and determine quality metrics like size, shape, color and defects. The proposed system would capture images, preprocess them, extract features and classify fruits as good or defective quality without damaging the fruits. This could help automate quality inspection and grading of agricultural produce.
Analysis of Near-infrared (NIR) spectroscopy for chlorophyll prediction in oi...journalBEEI
This document analyzes the use of near-infrared (NIR) spectroscopy to predict chlorophyll content in oil palm leaves as a non-invasive alternative to traditional chemical analysis methods. NIR spectral data from oil palm leaf samples were collected and correlated with chlorophyll readings using partial least squares regression with different preprocessing techniques. Savitzky-Golay smoothing produced the best results with an R^2 of 0.9998 for calibration and prediction, indicating NIR spectroscopy can accurately predict chlorophyll content in oil palm leaves in a time-saving manner without destructive sampling.
Feature extraction of Jabon (Anthocephalus sp) leaf disease using discrete wa...TELKOMNIKA JOURNAL
Jabon (Anthocephalus cadamba (Roxb.) Miq) is one type of forest plants that have very rapid growth until the process of the harvest. One inhibitor is a disease that attacks the leaves in the form of spots and blight that can cause death during the growth process of this tree. The purpose of this process is to detect the object of diseases that attack the leaves of jabon at the time in the nursery. Images of affected jabon leaf disease segmented by reducing the RGB color cylinders to separate the disease object from the background. Reduced channel G-R provides information in the form of disease areas contained in the image of Jabon leaf. Furthermore, the characteristics of leaf disease can be detected well using DWT in the 3-level decomposition process with SVM classification results that can separate both classes of spots and blight by 84.672%.
Herb Leaves Recognition using Gray Level Co-occurrence Matrix and Five Distan...IJECEIAES
This document describes a study on herb leaf recognition using the Gray Level Co-occurrence Matrix (GLCM) method of feature extraction and five distance-based similarity measures. The researchers tested recognition accuracy on 10, 20, and 30 types of herb leaves using GLCM features and the Canberra, Chebyshev, Cityblock, Euclidean, and Minkowski distances. They found the highest accuracy of 92% was achieved using GLCM features and the Canberra distance on 10 leaf images. Accuracy decreased to 50.67% and 60% when using 20 and 30 leaf images, respectively.
IRJET- Automatic Fruit Quality Detection SystemIRJET Journal
This document presents an automatic fruit quality detection system that uses computer vision and image processing techniques. The system captures images of fruits on a conveyor belt using a camera. It then performs image processing on the images to analyze features like color, size, and texture. It can detect defects in fruits based on pixel analysis of the images. The fruits are then sorted based on color and graded based on size. The system aims to automate and improve the efficiency of the fruit sorting and grading process compared to manual methods. It analyzes the images, detects quality factors, and controls hardware like the conveyor belt based on the analysis results.
IRJET- A Fruit Quality Inspection Sytem using Faster Region Convolutional...IRJET Journal
This document presents a fruit quality inspection system using a Faster Region Convolutional Neural Network (RCNN). The system uses a Faster RCNN algorithm and softmax classifier to detect and classify fruit quality. Faster RCNN is capable of performing both region proposal generation and objection tasks using a single convolutional network, making it faster than RCNN. The system feeds images of fruit into a convolutional network to generate feature maps. Region proposals are identified from the feature maps and reshaped to be classified and have bounding boxes predicted, allowing for automated fruit quality inspection.
Abstract In this paper illustrates the improvement of a low cost machine vision system using webcams and image processing algorithms for defect detection and sorting of tomatoes The sorting decision was based on three features extracted by the different image processing algorithms. This methodology based on the color features, which used for detecting the BER from good tomatoes. Two methods were developed for decision based sorting. The color image threshold method with shape factor was found efficient for differentiating good and defective tomatoes. The overall accuracy of defect detection attained was 94 and 96.5% respectively. Comparison of the results is also presented in this paper. Keywords: Dither Image, Stem Image, Histogram, Tomato.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Digital image processing methods for estimating leaf area of cucumber plantsnooriasukmaningtyas
Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.
IRJET- Detection of Plant Leaf Diseases using Image Processing and Soft-C...IRJET Journal
This document presents a method for detecting plant leaf diseases using image processing and soft computing techniques. It involves taking images of plant leaves using a digital camera, pre-processing the images, segmenting the images to identify infected regions, extracting features from the infected regions, and classifying the disease based on the features. The method was tested on various plant leaf image datasets with an accuracy of 63% and was able to identify diseases for tomatoes, corn, grapes, peaches and peppers. The automatic detection technique can help identify diseases at an early stage with less time and effort compared to manual detection methods.
IRJET- Image Processing based Detection of Unhealthy Plant LeavesIRJET Journal
This document describes a method for detecting unhealthy plant leaves using image processing and genetic algorithms. The method involves acquiring images of plant leaves, transforming the images to HSI color space, masking and removing green pixels, segmenting the leaves, extracting texture features, and using a genetic algorithm to classify leaves as healthy or unhealthy. The technique was tested on a database of 1000 plant leaf images with accurate results. It provides a fast and effective way to identify plant diseases compared to traditional expert observation methods.
A Novel Machine Learning Based Approach for Detection and Classification of S...IRJET Journal
This document presents a novel machine learning approach for detecting and classifying sugarcane plant diseases using discrete wavelet transform (DWT). Existing methods use MATLAB and support vector machines with limited accuracy. The proposed method uses DWT for image segmentation to identify affected plant regions. It acquires images, pre-processes for noise reduction, segments using edge detection, extracts features from regions of interest, classifies diseases, and detects diseases based on image analysis and classification. The goal is to more accurately detect diseases early to control crop damage and losses. A dataset of 13 sugarcane diseases is used to evaluate the approach.
A comparison of image segmentation techniques, otsu and watershed for x ray i...eSAT Journals
Abstract The most dangerous and rapidly spreading disease in the world is Tuberculosis. In the investigating for suspected tuberculosis (TB), chest radiography is the only key techniques of diagnosis based on the medical imaging So, Computer aided diagnosis (CAD) has been popular and many researchers are interested in this research areas and different approaches have been proposed for the TB detection. Image segmentation plays a great importance in most medical imaging, by extracting the anatomical structures from images. There exist many image segmentation techniques in the literature, each of them having their own advantages and disadvantages. The aim of X-ray segmentation is to subdivide the image in different portions, so that it can help during the study the structure of the bone, for the detection of disorder. The goal of this paper is to review the most important image segmentation methods starting from a data base composed by real X-ray images. Keywords— chest radiography, computer aided diagnosis, image segmentation, anatomical structures, real X-rays.
DETECTION OF NUTRIENT DEFICIENCIES IN CROPS USING SUPPORT VECTOR MACHINE (SVM)IRJET Journal
This document discusses a proposed method for detecting nutrient deficiencies in crop leaves using image processing and machine learning techniques. The method involves capturing leaf images, extracting color and texture features, storing feature data in training and testing databases, using k-means clustering to separate normal and deficient leaves, and applying k-NN classification to identify specific nutrient deficiencies by comparing testing features to the training database. The method is evaluated on a dataset of rice crop leaf images containing different nutrient deficiency types, with results showing the system can accurately detect deficiencies like boron deficiency and recommend appropriate remedies.
IRJET- Weedicide Spray Robot using Image ProcessingIRJET Journal
This document describes a proposed weedicide spray robot that uses image processing to identify and remove weeds without harming crops. It discusses:
1. The need for the robot due to labor shortages and the problems caused by weeds decreasing crop yields.
2. How the robot would work, using a camera to capture images of the field and identify weeds using computer vision techniques before precisely spraying herbicides only in those areas.
3. The goal of reducing herbicide usage and preserving the organic quality of crops compared to traditional spraying methods.
IRJET- A Review on Plant Disease Detection using Image ProcessingIRJET Journal
This document summarizes a research paper on detecting plant diseases from images using digital image processing techniques. The main steps discussed are: 1) Acquiring digital images of plant leaves, 2) Pre-processing the images by cropping, converting to grayscale, and enhancing, 3) Segmenting the images using k-means clustering to identify infected regions, 4) Extracting color, texture, and shape features from the segmented images, and 5) Classifying the images using a support vector machine to identify the type of disease. The proposed method was tested on images of citrus leaves to detect different diseases and future work aims to improve classification accuracy for other plant species.
IRJET- Detection and Classification of Leaf DiseasesIRJET Journal
This document presents a method for detecting and classifying leaf diseases using image processing techniques. The method involves 4 main phases: 1) Image preprocessing including noise removal and color space transformation. 2) Image segmentation using k-means clustering to separate healthy and infected tissue. 3) Feature extraction of texture characteristics. 4) Classification of the disease using a support vector machine model. The results diagnose the disease name and percentage of leaf area infected to help farmers quickly identify and respond to plant diseases.
IRJET- AI Based Fault Detection on Leaf and Disease Prediction using K-means ...IRJET Journal
This document discusses using k-means clustering and image processing techniques to detect faults and diseases on leaves. It aims to identify problem areas on leaves, calculate the ratio of faulty to normal areas, and predict the disease type. The document provides background on the importance of increasing food production despite challenges from crop diseases. It also reviews related work using image segmentation, feature extraction, and algorithms like k-means clustering, neural networks and support vector machines to analyze leaf images for disease detection. The proposed method uses k-means clustering on MATLAB to identify problem areas on leaves and calculate fault ratios to determine if leaves can be cured.
Plant Monitoring using Image Processing, Raspberry PI & IOTIRJET Journal
This document describes a plant monitoring system using image processing, a Raspberry Pi, and the Internet of Things. The system uses image processing techniques like segmentation, feature extraction and classification on images of plant leaves to detect diseases. Sensors connected to an Arduino board such as a humidity sensor, gas sensors and a light sensor are used to monitor environmental conditions. The Arduino and Raspberry Pi are connected to allow the sensors data to be sent to the Raspberry Pi. The Raspberry Pi then sends notifications about the plant health and environmental conditions to smartphones. This allows remote monitoring of farm conditions.
Plant Leaf Disease Detection and Classification Using Image ProcessingIRJET Journal
The document summarizes a research paper on detecting and classifying plant leaf diseases using image processing techniques. It begins by discussing the importance of identifying plant diseases early. It then provides an overview of traditional identification methods and their limitations. Next, it describes how image processing can be used to extract features from leaf images and classify diseases using machine learning algorithms. The paper evaluates several studies that have achieved accuracy ranging from 80-99.8% using different approaches. It also discusses challenges like variable image quality and limited datasets, and potential solutions. Finally, it presents results showing accuracy of 95-99% for different techniques depending on the dataset and diseases studied.
IRJET- Leaf Disease Detecting using CNN TechniqueIRJET Journal
This document describes a proposed system for detecting leaf diseases using convolutional neural network (CNN) techniques. The system uses image acquisition, pre-processing including cropping, resizing and filtering, segmentation using k-means clustering, feature extraction of color, texture and shape features, and classification using CNN. The system is tested on images of mango, pomegranate, guava and sapota leaves to automatically identify diseases and recommend appropriate control methods, providing an improvement over manual identification methods.
IRJET- An Expert System for Plant Disease Diagnosis by using Neural NetworkIRJET Journal
This document describes a proposed system to diagnose plant diseases using neural networks and image processing. The system would take an image of a plant leaf using a smartphone, extract features from the image like color, texture, and edges using preprocessing and segmentation algorithms. It would then use a support vector machine algorithm and the extracted features to predict the plant disease. It would also recommend pesticides and their costs to treat the predicted disease to help farmers identify effective treatment options. The goal is to develop an automated system to help identify plant diseases from images in order to benefit large-scale crop monitoring and disease detection.
IRJET- An Expert System for Plant Disease Diagnosis by using Neural NetworkIRJET Journal
This document describes a proposed system to diagnose plant diseases using neural networks and image processing. The system would take an image of a plant leaf using a smartphone, extract features from the image like color, texture, and edges using preprocessing and segmentation algorithms. It would then use a support vector machine algorithm and the extracted features to predict the plant disease. It would also recommend pesticides and their costs to treat the predicted disease to help farmers identify effective treatment options. The goal is to develop an automated system to help identify plant diseases from images in order to benefit large-scale crop monitoring and disease detection.
Extraction of texture features by using gabor filter in wheat crop disease de...eSAT Journals
This document discusses a method for detecting diseases in wheat crops using image processing and artificial neural networks. It involves taking digital images of wheat crop leaves and preprocessing the images by applying Gaussian and median filters to reduce noise. The images are then segmented using CIELAB color space. Texture features like area, perimeter, contrast, and energy are extracted from the images using Gabor filters. These features are then fed into an artificial neural network classifier to identify the type of disease present in the wheat crop. The method aims to help farmers more quickly and accurately detect diseases so they can better manage their crops and increase agricultural productivity.
IRJET- Crop Pest Detection and Classification by K-Means and EM ClusteringIRJET Journal
This document proposes a method for crop pest detection and classification using digital image processing techniques. The method uses K-means and EM clustering algorithms to segment cropped images based on color, then extracts features from the segmented regions. Support vector machines (SVM) are used to classify the pest types. The key steps are: 1) preprocessing images, 2) segmenting using K-means and EM clustering on color features, 3) extracting features from segmented regions, 4) classifying pest types using SVM. The goal is to automatically detect and identify crop pests, which could help farmers monitor fields and control pests early to increase crop yields.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Similar to Weed and crop segmentation and classification using area thresholding (20)
Mechanical properties of hybrid fiber reinforced concrete for pavementseSAT Journals
Abstract
The effect of addition of mono fibers and hybrid fibers on the mechanical properties of concrete mixture is studied in the present
investigation. Steel fibers of 1% and polypropylene fibers 0.036% were added individually to the concrete mixture as mono fibers and
then they were added together to form a hybrid fiber reinforced concrete. Mechanical properties such as compressive, split tensile and
flexural strength were determined. The results show that hybrid fibers improve the compressive strength marginally as compared to
mono fibers. Whereas, hybridization improves split tensile strength and flexural strength noticeably.
Keywords:-Hybridization, mono fibers, steel fiber, polypropylene fiber, Improvement in mechanical properties.
Material management in construction – a case studyeSAT Journals
Abstract
The objective of the present study is to understand about all the problems occurring in the company because of improper application
of material management. In construction project operation, often there is a project cost variance in terms of the material, equipments,
manpower, subcontractor, overhead cost, and general condition. Material is the main component in construction projects. Therefore,
if the material management is not properly managed it will create a project cost variance. Project cost can be controlled by taking
corrective actions towards the cost variance. Therefore a methodology is used to diagnose and evaluate the procurement process
involved in material management and launch a continuous improvement was developed and applied. A thorough study was carried
out along with study of cases, surveys and interviews to professionals involved in this area. As a result, a methodology for diagnosis
and improvement was proposed and tested in selected projects. The results obtained show that the main problem of procurement is
related to schedule delays and lack of specified quality for the project. To prevent this situation it is often necessary to dedicate
important resources like money, personnel, time, etc. To monitor and control the process. A great potential for improvement was
detected if state of the art technologies such as, electronic mail, electronic data interchange (EDI), and analysis were applied to the
procurement process. These helped to eliminate the root causes for many types of problems that were detected.
Managing drought short term strategies in semi arid regions a case studyeSAT Journals
Abstract
Drought management needs multidisciplinary action. Interdisciplinary efforts among the experts in various fields of the droughts
prone areas are helpful to achieve tangible and permanent solution for this recurring problem. The Gulbarga district having the total
area around 16, 240 sq.km, and accounts 8.45 per cent of the Karnataka state area. The district has been situated with latitude 17º 19'
60" North and longitude of 76 º 49' 60" east. The district is situated entirely on the Deccan plateau positioned at a height of 300 to
750 m above MSL. Sub-tropical, semi-arid type is one among the drought prone districts of Karnataka State. The drought
management is very important for a district like Gulbarga. In this paper various short term strategies are discussed to mitigate the
drought condition in the district.
Keywords: Drought, South-West monsoon, Semi-Arid, Rainfall, Strategies etc.
Life cycle cost analysis of overlay for an urban road in bangaloreeSAT Journals
Abstract
Pavements are subjected to severe condition of stresses and weathering effects from the day they are constructed and opened to traffic
mainly due to its fatigue behavior and environmental effects. Therefore, pavement rehabilitation is one of the most important
components of entire road systems. This paper highlights the design of concrete pavement with added mono fibers like polypropylene,
steel and hybrid fibres for a widened portion of existing concrete pavement and various overlay alternatives for an existing
bituminous pavement in an urban road in Bangalore. Along with this, Life cycle cost analyses at these sections are done by Net
Present Value (NPV) method to identify the most feasible option. The results show that though the initial cost of construction of
concrete overlay is high, over a period of time it prove to be better than the bituminous overlay considering the whole life cycle cost.
The economic analysis also indicates that, out of the three fibre options, hybrid reinforced concrete would be economical without
compromising the performance of the pavement.
Keywords: - Fatigue, Life cycle cost analysis, Net Present Value method, Overlay, Rehabilitation
Laboratory studies of dense bituminous mixes ii with reclaimed asphalt materialseSAT Journals
Abstract
The issue of growing demand on our nation’s roadways over that past couple of decades, decreasing budgetary funds, and the need to
provide a safe, efficient, and cost effective roadway system has led to a dramatic increase in the need to rehabilitate our existing
pavements and the issue of building sustainable road infrastructure in India. With these emergency of the mentioned needs and this
are today’s burning issue and has become the purpose of the study.
In the present study, the samples of existing bituminous layer materials were collected from NH-48(Devahalli to Hassan) site.The
mixtures were designed by Marshall Method as per Asphalt institute (MS-II) at 20% and 30% Reclaimed Asphalt Pavement (RAP).
RAP material was blended with virgin aggregate such that all specimens tested for the, Dense Bituminous Macadam-II (DBM-II)
gradation as per Ministry of Roads, Transport, and Highways (MoRT&H) and cost analysis were carried out to know the economics.
Laboratory results and analysis showed the use of recycled materials showed significant variability in Marshall Stability, and the
variability increased with the increase in RAP content. The saving can be realized from utilization of recycled materials as per the
methodology, the reduction in the total cost is 19%, 30%, comparing with the virgin mixes.
Keywords: Reclaimed Asphalt Pavement, Marshall Stability, MS-II, Dense Bituminous Macadam-II
Laboratory investigation of expansive soil stabilized with natural inorganic ...eSAT Journals
This document summarizes a study on stabilizing expansive black cotton soil with the natural inorganic stabilizer RBI-81. Laboratory tests were conducted to evaluate the effect of RBI-81 on the soil's engineering properties. The tests showed that with 2% RBI-81 and 28 days of curing, the unconfined compressive strength increased by around 250% and the CBR value improved by approximately 400% compared to the untreated soil. Overall, the study found that RBI-81 effectively improved the strength properties of the black cotton soil and its suitability as a soil stabilizer was supported.
Influence of reinforcement on the behavior of hollow concrete block masonry p...eSAT Journals
Abstract
Reinforced masonry was developed to exploit the strength potential of masonry and to solve its lack of tensile strength. Experimental
and analytical studies have been carried out to investigate the effect of reinforcement on the behavior of hollow concrete block
masonry prisms under compression and to predict ultimate failure compressive strength. In the numerical program, three dimensional
non-linear finite elements (FE) model based on the micro-modeling approach is developed for both unreinforced and reinforced
masonry prisms using ANSYS (14.5). The proposed FE model uses multi-linear stress-strain relationships to model the non-linear
behavior of hollow concrete block, mortar, and grout. Willam-Warnke’s five parameter failure theory has been adopted to model the
failure of masonry materials. The comparison of the numerical and experimental results indicates that the FE models can successfully
capture the highly nonlinear behavior of the physical specimens and accurately predict their strength and failure mechanisms.
Keywords: Structural masonry, Hollow concrete block prism, grout, Compression failure, Finite element method,
Numerical modeling.
Influence of compaction energy on soil stabilized with chemical stabilizereSAT Journals
This document summarizes a study on the influence of compaction energy on soil stabilized with a chemical stabilizer. Laboratory tests were conducted on locally available loamy soil treated with a patented polymer liquid stabilizer and compacted at four different energy levels. The study found that increasing the compaction effort increased the density of both untreated and treated soil, but the rate of increase was lower for stabilized soil. Treating the soil with the stabilizer improved its unconfined compressive strength and resilient modulus, and reduced accumulated plastic strain, with these properties further improved by higher compaction efforts. The stabilized soil exhibited strength and performance benefits compared to the untreated soil.
Geographical information system (gis) for water resources managementeSAT Journals
This document describes a hydrological framework developed in the form of a Hydrologic Information System (HIS) to meet the information needs of various government departments related to water management in a state. The HIS consists of a hydrological database coupled with tools for collecting and analyzing spatial and non-spatial water resources data. It also incorporates a hydrological model to indirectly assess water balance components over space and time. A web-based GIS portal was created to allow users to access and visualize the hydrological data, as well as outputs from the SWAT hydrological model. The framework is intended to facilitate integrated water resources planning and management across different administrative levels.
Forest type mapping of bidar forest division, karnataka using geoinformatics ...eSAT Journals
Abstract
The study demonstrate the potentiality of satellite remote sensing technique for the generation of baseline information on forest types
including tree plantation details in Bidar forest division, Karnataka covering an area of 5814.60Sq.Kms. The Total Area of Bidar
forest division is 5814Sq.Kms analysis of the satellite data in the study area reveals that about 84% of the total area is Covered by
crop land, 1.778% of the area is covered by dry deciduous forest, 1.38 % of mixed plantation, which is very threatening to the
environmental stability of the forest, future plantation site has been mapped. With the use of latest Geo-informatics technology proper
and exact condition of the trees can be observed and necessary precautions can be taken for future plantation works in an appropriate
manner
Keywords:-RS, GIS, GPS, Forest Type, Tree Plantation
Factors influencing compressive strength of geopolymer concreteeSAT Journals
Abstract
To study effects of several factors on the properties of fly ash based geopolymer concrete on the compressive strength and also the
cost comparison with the normal concrete. The test variables were molarities of sodium hydroxide(NaOH) 8M,14M and 16M, ratio of
NaOH to sodium silicate (Na2SiO3) 1, 1.5, 2 and 2.5, alkaline liquid to fly ash ratio 0.35 and 0.40 and replacement of water in
Na2SiO3 solution by 10%, 20% and 30% were used in the present study. The test results indicated that the highest compressive
strength 54 MPa was observed for 16M of NaOH, ratio of NaOH to Na2SiO3 2.5 and alkaline liquid to fly ash ratio of 0.35. Lowest
compressive strength of 27 MPa was observed for 8M of NaOH, ratio of NaOH to Na2SiO3 is 1 and alkaline liquid to fly ash ratio of
0.40. Alkaline liquid to fly ash ratio of 0.35, water replacement of 10% and 30% for 8 and 16 molarity of NaOH and has resulted in
compressive strength of 36 MPa and 20 MPa respectively. Superplasticiser dosage of 2 % by weight of fly ash has given higher
strength in all cases.
Keywords: compressive strength, alkaline liquid, fly ash
Experimental investigation on circular hollow steel columns in filled with li...eSAT Journals
Abstract
Composite Circular hollow Steel tubes with and without GFRP infill for three different grades of Light weight concrete are tested for
ultimate load capacity and axial shortening , under Cyclic loading. Steel tubes are compared for different lengths, cross sections and
thickness. Specimens were tested separately after adopting Taguchi’s L9 (Latin Squares) Orthogonal array in order to save the initial
experimental cost on number of specimens and experimental duration. Analysis was carried out using ANN (Artificial Neural
Network) technique with the assistance of Mini Tab- a statistical soft tool. Comparison for predicted, experimental & ANN output is
obtained from linear regression plots. From this research study, it can be concluded that *Cross sectional area of steel tube has most
significant effect on ultimate load carrying capacity, *as length of steel tube increased- load carrying capacity decreased & *ANN
modeling predicted acceptable results. Thus ANN tool can be utilized for predicting ultimate load carrying capacity for composite
columns.
Keywords: Light weight concrete, GFRP, Artificial Neural Network, Linear Regression, Back propagation, orthogonal
Array, Latin Squares
Experimental behavior of circular hsscfrc filled steel tubular columns under ...eSAT Journals
This document summarizes an experimental study that tested circular concrete-filled steel tube columns with varying parameters. 45 specimens were tested with different fiber percentages (0-2%), tube diameter-to-wall-thickness ratios (D/t from 15-25), and length-to-diameter (L/d) ratios (from 2.97-7.04). The results found that columns filled with fiber-reinforced concrete exhibited higher stiffness, equal ductility, and enhanced energy absorption compared to those filled with plain concrete. The load carrying capacity increased with fiber content up to 1.5% but not at 2.0%. The analytical predictions of failure load closely matched the experimental values.
Evaluation of punching shear in flat slabseSAT Journals
Abstract
Flat-slab construction has been widely used in construction today because of many advantages that it offers. The basic philosophy in
the design of flat slab is to consider only gravity forces; this method ignores the effect of punching shear due to unbalanced moments
at the slab column junction which is critical. An attempt has been made to generate generalized design sheets which accounts both
punching shear due to gravity loads and unbalanced moments for cases (a) interior column; (b) edge column (bending perpendicular
to shorter edge); (c) edge column (bending parallel to shorter edge); (d) corner column. These design sheets are prepared as per
codal provisions of IS 456-2000. These design sheets will be helpful in calculating the shear reinforcement to be provided at the
critical section which is ignored in many design offices. Apart from its usefulness in evaluating punching shear and the necessary
shear reinforcement, the design sheets developed will enable the designer to fix the depth of flat slab during the initial phase of the
design.
Keywords: Flat slabs, punching shear, unbalanced moment.
Evaluation of performance of intake tower dam for recent earthquake in indiaeSAT Journals
Abstract
Intake towers are typically tall, hollow, reinforced concrete structures and form entrance to reservoir outlet works. A parametric
study on dynamic behavior of circular cylindrical towers can be carried out to study the effect of depth of submergence, wall thickness
and slenderness ratio, and also effect on tower considering dynamic analysis for time history function of different soil condition and
by Goyal and Chopra accounting interaction effects of added hydrodynamic mass of surrounding and inside water in intake tower of
dam
Key words: Hydrodynamic mass, Depth of submergence, Reservoir, Time history analysis,
Evaluation of operational efficiency of urban road network using travel time ...eSAT Journals
This document evaluates the operational efficiency of an urban road network in Tiruchirappalli, India using travel time reliability measures. Traffic volume and travel times were collected using video data from 8-10 AM on various roads. Average travel times, 95th percentile travel times, and buffer time indexes were calculated to assess reliability. Non-motorized vehicles were found to most impact reliability on one road. A relationship between buffer time index and traffic volume was developed. Finally, a travel time model was created and validated based on length, speed, and volume.
Estimation of surface runoff in nallur amanikere watershed using scs cn methodeSAT Journals
Abstract
The development of watershed aims at productive utilization of all the available natural resources in the entire area extending from
ridge line to stream outlet. The per capita availability of land for cultivation has been decreasing over the years. Therefore, water and
the related land resources must be developed, utilized and managed in an integrated and comprehensive manner. Remote sensing and
GIS techniques are being increasingly used for planning, management and development of natural resources. The study area, Nallur
Amanikere watershed geographically lies between 110 38’ and 110 52’ N latitude and 760 30’ and 760 50’ E longitude with an area of
415.68 Sq. km. The thematic layers such as land use/land cover and soil maps were derived from remotely sensed data and overlayed
through ArcGIS software to assign the curve number on polygon wise. The daily rainfall data of six rain gauge stations in and around
the watershed (2001-2011) was used to estimate the daily runoff from the watershed using Soil Conservation Service - Curve Number
(SCS-CN) method. The runoff estimated from the SCS-CN model was then used to know the variation of runoff potential with different
land use/land cover and with different soil conditions.
Keywords: Watershed, Nallur watershed, Surface runoff, Rainfall-Runoff, SCS-CN, Remote Sensing, GIS.
Estimation of morphometric parameters and runoff using rs & gis techniqueseSAT Journals
This document summarizes a study that used remote sensing and GIS techniques to estimate morphometric parameters and runoff for the Yagachi catchment area in India over a 10-year period. Morphometric analysis was conducted to understand the hydrological response at the micro-watershed level. Daily runoff was estimated using the SCS curve number model. The results showed a positive correlation between rainfall and runoff. Land use/land cover changes between 2001-2010 were found to impact estimated runoff amounts. Remote sensing approaches provided an effective means to model runoff for this large, ungauged area.
Effect of variation of plastic hinge length on the results of non linear anal...eSAT Journals
Abstract The nonlinear Static procedure also well known as pushover analysis is method where in monotonically increasing loads are applied to the structure till the structure is unable to resist any further load. It is a popular tool for seismic performance evaluation of existing and new structures. In literature lot of research has been carried out on conventional pushover analysis and after knowing deficiency efforts have been made to improve it. But actual test results to verify the analytically obtained pushover results are rarely available. It has been found that some amount of variation is always expected to exist in seismic demand prediction of pushover analysis. Initial study is carried out by considering user defined hinge properties and default hinge length. Attempt is being made to assess the variation of pushover analysis results by considering user defined hinge properties and various hinge length formulations available in literature and results compared with experimentally obtained results based on test carried out on a G+2 storied RCC framed structure. For the present study two geometric models viz bare frame and rigid frame model is considered and it is found that the results of pushover analysis are very sensitive to geometric model and hinge length adopted. Keywords: Pushover analysis, Base shear, Displacement, hinge length, moment curvature analysis
Effect of use of recycled materials on indirect tensile strength of asphalt c...eSAT Journals
Abstract
Depletion of natural resources and aggregate quarries for the road construction is a serious problem to procure materials. Hence
recycling or reuse of material is beneficial. On emphasizing development in sustainable construction in the present era, recycling of
asphalt pavements is one of the effective and proven rehabilitation processes. For the laboratory investigations reclaimed asphalt
pavement (RAP) from NH-4 and crumb rubber modified binder (CRMB-55) was used. Foundry waste was used as a replacement to
conventional filler. Laboratory tests were conducted on asphalt concrete mixes with 30, 40, 50, and 60 percent replacement with RAP.
These test results were compared with conventional mixes and asphalt concrete mixes with complete binder extracted RAP
aggregates. Mix design was carried out by Marshall Method. The Marshall Tests indicated highest stability values for asphalt
concrete (AC) mixes with 60% RAP. The optimum binder content (OBC) decreased with increased in RAP in AC mixes. The Indirect
Tensile Strength (ITS) for AC mixes with RAP also was found to be higher when compared to conventional AC mixes at 300C.
Keywords: Reclaimed asphalt pavement, Foundry waste, Recycling, Marshall Stability, Indirect tensile strength.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Weed and crop segmentation and classification using area thresholding
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 375
WEED AND CROP SEGMENTATION AND CLASSIFICATION USING
AREA THRESHOLDING
Su Hnin Hlaing1
, Aung Soe Khaing2
1
Demonstrator, Department of Electronic Engineering, Mandalay Technological University, Myanmar
2
Associate Professor, Department of Electronic Engineering, Mandalay Technological University, Myanmar
Abstract
In the agricultural industry, the weed and crop identification and classification are major technical and economical
importance. Two classification algorithms are focused in this paper. And the better classification algorithm has been selected
to classify weed and crop from the images. There are three main parts of proposed system are segmentation, classification and
error calculation. The developed algorithm based on area thresholding has been tested on weeds and various locations. Forty
one sample images have been tested and the result of some weed coverage rate is illustrated. Moreover, the misclassification
rate is also computed. An algorithm has been done to automate the tasks of segmentation and classification. The overall
process is implemented in MATLAB.
Keywords - Objects segmentation, Image processing, Plant classification, Area Thresholding
--------------------------------------------------------------------***----------------------------------------------------------------------
1. INTRODUCTION
As weeds are frequently distributed non -uniformly within a
field, weeding is a very hard work [1]. Every year a large
amount of herbicide is used for removing weeds from
agricultural fields which is not only expensive but also a
source of environmental pollution. Moreover, both costly
and time consuming is caused due to hand labor [4].
Therefore, weed control is a necessary management practice
in agricultural systems, which is critical to sustain crop
productivity and quality [1]. Farmers need alternatives for
weed control due to the desire to reduce chemical use and
production costs [7]. For instance when growing vegetables
such as carrots, onions and cabbage the weeds can be
removed by special harrows but it cannot support for
economic system [3].
Nowadays, uniform spraying is the most common method
for herbicides application. However, this method is
inefficient and cost-ineffective as weed distribution is
usually non-uniform and highly aggregated in clumps within
the arable field [1]. There could be many parts of the field
that have none or insignificant volume of weeds. This
property of the weed distributions makes the development of
site-specific management feasible [8]. In this management,
the amount of herbicides applied is reduced through
spraying only the weed infested area of a field, where
different selective herbicides with corresponding application
rates are applied to control weed differently [5]. The
machine vision based approach uses shape, texture, color
and location based features individually of jointly to
discriminate between weed and crop [11]. In the other
research, Caltrans sprays roadside plant material with
herbicide to prevent the weeds from becoming a fire hazard
during the summer. The first step in identifying weeds
within an image involves classifying the pixels [12]. The
pixels shall be classified using a point operation. The
surrounding pixels will not bias a pixel‟s classification. The
purpose of segmenting the image into plant and background
pixels is to detect the amount of plant material within a
specific area [12]. If the amount of plant material reaches a
specific threshold, the area is targeted for herbicidal spray
application [12]. The spray threshold is set too close to the
background misclassification rate, then herbicide will be
wasted spraying background. Therefore, a larger
misclassification rate limits the smallest plant that can be
detected without targeting the background for spray [12].
A system that could make use of the spatial distribution
information in real-time and apply only the necessary
amounts of herbicide to the weed-infested area would be
much more efficient and minimize environmental damage.
Therefore, a high spatial resolution, real-time weed
infestation detection system seems to be the solution for
site-specific weed management.
In this paper, three main parts presented for weed and crop
classification. It has got (1) Image acquisition and gray
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 376
transformation, (2) Image segmentation and noise removal,
and (3) Classification of weed and crop [2]. Two
classification methods are studied in this research. They are
classification based on intensity method and area
thresholding classification method. The first method is
depended on the intensity of the images [13]. However, this
algorithm cannot operate to classify weed and crop
automatically. So, the area thresholding method is used to
get reliable classified images. These methods are executed
depending on size of the plants. By using digital camera, the
input image is obtained. Most of images are needed to
change from the color images into grayscale images for easy
and fast processing. The segmentation step implemented by
using thresholding method. In the classification part, the
areas of segmented image are then compared with the
selected threshold for classification of weeds and crop [6].
2. MATERIALS AND METHODS
The first algorithm consists of five fractions: (i)
preprocessing, (ii) Binarization using Otsu‟s thresholding,
(iii) Marker control watershed segmentation, (iv) Gray
transformation and (v) classification based on intensity. The
simple weed and crop images are used to test the system.
Firstly, the color images are converted to gray scale images
for easy and fast processing [13]. Median filter is applied to
the gray image to reduce the amount of data. Otsu‟s method
is used to filtered image for converting black and white
image. This method chooses the optimal threshold to
minimize the intraclass variance of the black and white
pixels [14]. Then, binarized image is divided into different
regions according to watershed segmentation method. The
sobel operator is applied on the binary image to find the
gradient magnitude. By using this magnitude, estimate the
watershed transform that divide different regions. The
segmented image is changed into RGB segmented image to
distinct region. To classify weed and crop, the RGB image
is converted into gray image. Finally, the intensity value is
defined by manually to extract weed plant from the image.
The results image is described in Fig- 3. The intensity values
of the images can change due to light, dark, weather
condition and camera situation. Therefore, these values must
be defined for every weed and crop images. However, the
next algorithm can detect without manual extraction as can
be seen their results image.
The area thresholding classification system includes the five
steps. They are Excess Green Gray transformation,
segmentation, label the image, removing the unwanted data
and classification based on area thresholding. The system
block diagram of the system is illustrated in Fig-1, which
consists of three main steps:
1) The Excess green gray transformation 2G-R-B is
executed to easy and fast processing for
segmentation stage.
2) Background and plants of the image is separated
according to the gray index.
3) Extraction of weed and crop from the segmented
image by area thresholding.
And then the detailed algorithms of the system are also
described into step by step.
2.1 Image Acquisition
In this research, the weed and crop color images are
acquired through the digital camera. The sample images are
captured in the fields. Images are obtained at different times
of a day. Moreover, weeds and crop with varying canopy
size were selected to increase the difficulty of the
classification problem. The weed and crop images are taken
at an angle 45 degree with the ground in natural lighting
conditions with digital camera. The camera is mounted in
the height of 2.15m from the ground. The resolution of the
camera is set to 3648×2736 pixels during image capturing.
In the experimental analysis, all the images were resized to a
resolution of 320×240 pixels in order to reduce the
computation time [10]. Fig-2 illustrates sample weed and
crop images taken from the fields.
Input Image
Gray Transformation (ExG)
Binarization
Filtering
Labeling
Area Thresholding
Output Detected Image
Fig-1: Block diagram of the system
3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 377
(a) (b)
(c) (d)
Fig-2: The original images of the crop and some weed that
captured by using camera: (a) rape plant, (b) Lanchon, (c)
Pigweed and (d) Kyaut kut.
Fig-3: Sample result of classification based on intensity
method: Original image (up); Detected weed image (bottom-
left); Detected crop image (bottom-right).
2.2 Gray Transformation
The gray transformation processing is to turn the color
image to the gray image. The purpose of gray
transformation is to reduce the amount of color data in the
image so as to speed up the following processing. The color
difference between plants and background in the color
images should be kept as well as possible in the gray image.
Equation (1) is used for gray transformation in image
processing. Assuming the coordinate (x, y) is the pixel
point.
EXG(x, y) =2g-r-b (1)
Where, r= , g= , b=
In this equation R, G and B are the three components of
pixel color in RGB color space; ExG is the transformation
result, a gray value. The intensity information is highlighted
and discarded most color information in the color image
using equation (1). The pixel values in red channel and blue
channel color space are always less than the in green color.
Therefore, the Excess green feature is used to extract the
distinct color channel. The result of this algorithm is
illustrated in Fig- 4.
(a) The original input image
4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 378
(b) Result of the Green minus red image
(c) Result of the green minus blue image
Fig-4: Gray transformation
2.3 Segmentation
To remove background of the image, different techniques
such as thresholding-based segmentation, edge-based
segmentation, Color-based segmentation and watershed
segmentation can be used. In this study, gray removal
segmentation method is used for the segmentation task. All
the segmentation steps are done depending on the binary
image. Therefore, the grayscale image is converted into
binary image from the preprocessing stage. Thresholding
based on gray image is used to change the binary image. By
using the following equation
(2)
For all pixels in the original image the absolute values of
green minus red and green minus blue are calculated. These
give measurements of the pixels distance to the grayscale
line. If both of these distance values are greater than
threshold (T), the pixel is classified as plant (P). If none or
only one is greater than T, the pixel is classified as
background (Bg). The number of T is equivalent to the
threshold in the above mentioned index methods and has to
be chosen based on the available image material. For the
image of this paper T=20 has proven to give a very good
result. This method of removing pixels of high „greyness‟
can be seen as calculating two separate indices and then
requiring that a pixel is on the correct side of the threshold
in both cases to be classified as P. The result of binary
images includes the small noise as misclassified plant pixels.
Therefore each pixel of the segmented images is labeled
with a value according to the component it is assigned to.
After that the property of the region is defined into area.
Then the value of region is firmed to find the minimum area.
And then the minimum area of the region is removed from
the image as shown in fig-5. The 40 pixels of minimum
areas are discarded in this filtering stage.
(a)
(b)
Fig-5: Result of segmented (a) binary image and (b) filtered
image
2.4 Classification
The final segmented image is used as the input of the weed
and crop classification stage. Weed detection is executed
depended on their areas. Firstly, the segmented binary image
is labeled using 8-connected components. In this way, it can
easy to evaluate the areas of the objects. And then, a
threshold value is selected to classify weed plants and crop
plants. Therefore, threshold value is set at 6000 for this
research. The crop plants are mostly large the weed plants in
related to the testing. Therefore, the weed plant is detected
when the object areas are less than threshold value. In the
other way, the remaining objects are classified as crop
plants. The areas of the individual objects are calculated
according to the equation (3). In this equation, F (j, k) is
binary segmented image, xj and yk are scaled coordinates in
row and column and J and K are row and column of binary
5. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 379
image. The detected weed and crop images are obtained by
comparing the threshold value and their areas.
(3)
The desired points of the image (xj and yk) are obtained
using the following equation:
xj =j+1/2
yk = k+1/2
The thresholding equation is illustrated in equation (4). The
optimal threshold value is selected at 6000.
(4)
(a) Result of detected weed image
(b) Result of detected crop image
(d) Result of classified image
Fig-6: Result of weed and crop classified images
If the sizes of the crop plants are equal to the weed plants,
the classification algorithm can cause the misclassification.
Therefore, those plants are misclassified results as can be
seen in Fig. 6. This original image consists of two crop
plants and four weeds plants. Among the two crop plants,
the area of the one crop plant is less than the system
threshold value. So, the misclassified image is obtained as
can be seen the Fig-7. (b).
(a) Original weed and crop image
(b) Detected weeds image with one crop is
misclassified as weed
(c) Detected crop image
(d) Misclassified image
Fig-7: Result of misclassified weed and crop images.
),()()(
1
),(
1 1
kjFyx
KJ
nmM n
k
J
j
K
k
m
jnm
6. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 380
Table- 1: The Experimental Results of Area Thresholding
Num
Total
Area
Weed
Area
Plants
Coverage
Detected
weed
Coverage
1 21986 6574 28.60% 9.19%
2 9029 17260 22.50% 12.60%
3 9069 17406 22.70% 12.60%
4 10985 2891 14.30% 3.80%
In the Table1, plants coverage and detected weed coverage
rate are defined as:
(5)
(6)
In Table 1, data describes that the percent of the plants
coverage and detected weed coverage of the results images.
According to the above equations SUMw is the sum of all
weed pixels and Zone Area is the pixel count in the whole
image, and is equal to the row × column of the image pixels.
As a result, almost of 13% of weed coverage is found in the
sample images.
er= (7)
Where, er = error rate or misclassification rate
Nw = number of detected weeds
Nm = number of misclassified crop
Above the error equation (7) is used to compute the
misclassification rate. As the sample results is illustrated in
Table II. For example, the first image in this table has six
weed plants and three crop plants. But the number of output
detected weed plants are nine. So, all the crop plants are
defined as weed plants. Therefore, 33.3% error rate is
obtained when these misclassified plants is evaluated
according to the equation (7). The remaining error rate can
be generated in the same way.
3. RESULTS AND DISCUSSION
This paper presents two main algorithms for weed and crop
classification system. Fig-6 and 7 describe the classified
result images. The plants with blue color are defined as
weeds and the red plants are classified as crops. The given
algorithm processes two types of images for weed and crop.
The algorithm produced reliable classified images to be
sprayed. In the field, almost the images that captured
according to image acquisition step can be classified to give
to the spray system. By using the resultant image, the weed
position can determined according to the pixels of left to
right and top to bottom. Although the system could classify
weed to be sprayed, darker images cause more errors in the
segmentation and also in later steps of the algorithm. When
the weed and crop plants are overlapped, this method cannot
classify weed and crop. The 41 sample images have been
used to test in this study. Among them, 7 images are found
the misclassification. Thus the proposed algorithm is
suitable for weed and crop segmentation and classification.
It will support to get the reliable results in real-time
application.
Table-2: Error Calculations of misclassified Weed and Crop
Images for the Area Thresholding Algorithm
No.
Number
of
detected
weeds
Number of
misclassified
crop
Missed
weeds
Weed mis-
classifications
rate
1 9 3 0 33.30%
2 6 1 0 17%
3 5 1 0 20%
4 4 1 0 25%
5 5 0 0 0%
In this Table 2, error calculations have been executed by
manually counting the number of misclassifications in the
randomly chosen result images. The missed weeds in this
Table II are weeds that were not fully marked in red or blue.
The weed misclassifications shows the objects classified as
weeds that are actually part of crop plant. Both weed area is
greater than threshold value and crop area is less than
threshold value can cause the misdetection. Although the
second system has some drawback, the better classification
results could generated with small error.
4. CONCLUSIONS
In this research, segmentation method and classification
based on area thresholding method are developed. Excess
green gray transformation (ExG) and area thresholding
algorithms are combined to obtain the exactly classified
images. The system shows an effective and reliable
classification of images captured by a camera. The image
segmentation algorithm is very useful method in the image
processing and it is very helpful for the subsequent
processing. When the plants are separated from each other
in the images, the results have been shown to be better. Also
the lighting conditions are important to be able to make a
reliable analysis.
7. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 381
ACKNOWLEDGMENT
The authors wish to thank to the head of Department of
Electronic Engineering, Mandalay Technological
University. The author would like to express special
appreciation and heartfelt thanks to her supervisor, Dr. Aung
Soe Khaing, Department of Electronic Engineering,
Mandalay Technological University for giving
understanding, helpful guidance, suggestion and directions
throughout the preparation of work. The author is also
sincerely thankful to all her teachers, Department of
Electronic Engineering, Mandalay Technological
University.
REFERENCES
[1]. Faisal Ahmed, Hasanul Kabir, Shayla Azad Bhuyan,
Hossain Bari, and Eman Hossain, “Automated Weed
Classification with Local Pattern-Based Texture
Descriptors”, International Arab Journal of
Information Technology, vol.9, no.4, pp. 382-391.
[2]. Irshad Ahmad, Abdul Muhamin, Muhammad.Islam
and Shahid Nawaz,”Weed Classification using
Histogram Maxima with Threshold for Selective
Herbicide Applications”, World Academy of science,
Engineering and Technology 2007.
[3]. Siddiqi M., Sulaiman S., Faye I., and Ahmad I., “A
Real Time Specific Weed Discrimination System
Using Multi-level Wavelet Decomposition”,
International Journal of Agriculture and Biology,
vol.11, issue 5, pp.559-565,2009.
[4]. Hossein Nejati, Zohreh Azimifar and Mohsen
Zamani,”Using Fast Fourier Transform for Weed
Detection in Corn Fields”, IEEE International
Conference on Systems, Man and Cybernetics (SMC
2008).
[5]. Mahammad Hameed Siddiqi, Sungyoung. Lee, Young-
Koo Lee, “Efficient Algorithm for Real-Time Specific
Weed leaf Classification System”, Journal of
Communication and Computer, ISSN 1548-7709,
USA.
[6]. S.Kiani and A.Jafari, “Crop Detection and Positioning
in the field Using Discriminant Analysis and Neural
Networks Based on shape features”, J-Agr.Sci.Tech
(2012) Vol.14:755-765.
[7]. Sajad KIANI, “Discriminating the Corn Plants from
the Weeds By Using Artificial Neural Networks”,
International Joural of Natural and Engineering
Sciences 6 (3): 55-58, 2012 ISSN: 1307-1149,E-ISSN:
2146-0086, WWW.nobel.gen.tr.
[8]. Tian L., Reid J., and Gerhards R., “Development of a
Precision Sprayer for Site-specific Weed
Management”, Transactions of the ASAE, vol.42,
issue 4, pp.893-900, 1999.
[9] DIAO ZHIHUA, WANG HUAN, SONG YINMAO,
and WANG YUNPENG, “Image Segmentation
Method for Cotton Mite Disease Based on Color
Features and Area Thresholding”, Journal of
Theoretical and Applied Information Technology, 10th
February 2013.Vol. 48 No.1, ISSN: 1992-8645.
[10]. W.S. LEE, D. C. SLAUGHTER, AND D.K. GILES,
“Robotic Weed Control System for Tomatoes”,
Precision Agriculture, 1, 95-113(1999)
[11]. Anant Bhardwaj, Manpreet Kaur, and Anupam Kumar,
“Recognition of Plants by Leaf Image using Moment
Invariant and Texture Analysis”, International Journal
of Innovation and Applied Studies ISSN 2028-9324
Vol.3 No.1 May 2013, pp.237-248, http://www.issr-
journals.org/ijias/
[12]. Chris Gliever, EEC206 Project Report, “Color
Segmentation of Plant and soil”. 3177 EUII, Friday
5PM, March 21th
2003.
[13]. Irshad Ahmad, Muhammad, Iram Fatima, Sungyoung
Lee, Young-Koo Lee, “Weed Classification Based on
Haar Wavelet Transform via k-Nearest Neighbor (k-
NN) for Real-Time Automatic Sprayer Control
System”, ICUMC 11, February 21-23,2011, Seoul,
Korea.
[14]. Mehmet Sezgin, Bulent Sankur. “Survey over Image
Thresholding Techniques and Quantitative
Performance Evaluation”, Journal of Electronic
Imaging.13 (1): 146-165, 2004.
BIOGRAPHIES
Su Hnin Hlaing received her
Bachelor of Engineering in
Electronic Communication
Technology from the Mandalay
Technological University,
Myanmar in 2008. She is now
master student in the Mandalay
Technological University,
Myanmar. Her research interests
include image processing, weed
and crop segmentation and
classification.
Aung Soe Khaing received his
PhD in Electronic Engineering
from Mandalay Technological
University, Mandalay,
Myanmar, in 2011. He was
doing research on Spatial
Frequency Analysis of the
Human Brain at the Institute of
Biomedical Engineering and
Informatics, Technical
University Ilmenau, Germany.
He is now Associate Professor at
8. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 382
Department of Electronic
Engineering, Mandalay
Technological University,
Mandalay, Myanmar. His
research interests include
computer based
Electrocardiogram (ECG)
system, biomedical signal and
image processing,
bioinstrumentation and
telemedicine.