With the advancement of digital image processing in agriculture and crop cultivation, imaging techniques are adopted to acquire real-time health status. Out of all the parts of plants, the leaf is the direct indicator of its health status, and hence applying various image processing approaches could benefit the process of yielding informative cases of plant health. At present, there are various approaches, e.g., feature extraction, segmentation, identification, the classification being evolved up with more dependencies being found in using machine learning; the studies show many contributions towards this challenge. However, it is not yet conclusive to understand the optimal approach. Hence, this paper highlights an explicit strength and weakness associated with the existing approaches existing imaging processing techniques to identify the disease condition from an input of plant leaves' image. The study also contributes to highlighting open-end research problems to have conclusive remarks about effectiveness.
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
This document discusses a proposed loss-fused convolutional neural network model for identifying and classifying plant disease. The model aims to improve predictive performance by combining the advantages of two different loss functions. The model was tested on a dataset from the Plant Village Database and achieved 98.93% accuracy in discriminating between affected and unaffected plant leaf samples, outperforming other existing methodologies. The paper provides background on plant disease detection techniques and reviews related work applying machine learning and deep learning methods.
Food is one of the basic needs of human being. Population is increasing day by day. So, it has become important to grow sufficient amount of crops to feed such a huge population. Agricultural intervention in the livelihood of rural India is about 58%. But with the time passing by, plants are being affected with many kinds of diseases, which cause great harm to the agricultural plant productions. It is very difficult to monitor the plant diseases. It requires tremendous amount of work, expertise in the plant diseases, and also require the excessive processing speed and time. Hence, image processing is used for the detection of plant diseases by just capturing the images of the leaves and comparing it with the data sets available. Latest and fostering technologies like Image processing is used to rectify such issues very effectively. In this project, four consecutive stages are used to discover the type of disease. The four stages include pre-processing, leaf segmentation, feature extraction and classification. This paper aims to support and help the farmers in an efficient way.
IRJET - Paddy Crop Disease Detection using Deep Learning Technique by using D...IRJET Journal
This document describes a study that uses deep learning and digital image processing techniques to detect diseases in paddy crops. The proposed system takes digital images of affected leaf parts as input and outputs the name of the detected disease and recommended pesticides. It first preprocesses and enhances the images before using edge detection, segmentation, and classification algorithms to analyze leaf parameters and identify diseases. The goal is to enable accurate, automated disease detection to help farmers protect crops and minimize pesticide usage.
This document presents a method for recognizing five fungal diseases of wheat (leaf rust, stem rust, yellow rust, powdery mildew, and septoria) using deep learning on images. A dataset of 2414 wheat disease images (WFD2020) was created with over 80% representing single diseases and over 12% healthy plants. A convolutional neural network with the EfficientNet architecture achieved the best accuracy of 94.2% for identification. The recognition method was implemented as a Telegram bot, allowing users to assess wheat plants for disease in field conditions using mobile devices.
Plant Disease Detection and Identification using Leaf Images using deep learningIRJET Journal
The document discusses a method for plant disease detection and identification using deep learning on leaf images. The method involves collecting images of plant leaves, preprocessing the images through steps like segmentation and grayscale conversion, and classifying plant diseases using a convolutional neural network (CNN) classifier. The goal is to develop an automated system for early and accurate detection of plant diseases to improve crop productivity and help farmers. The system could help reduce costs and time compared to traditional expert-based identification methods. Experimental results found the CNN achieved over 99% accuracy in identifying three common pomegranate diseases from leaf images.
Survey on Plant Disease Detection using Deep Learning based FrameworksAI Publications
Early identification of plant diseases is crucial as they can hinder the growth of their respective species. Although many machine learning models have been utilised for detecting and classifying plant diseases. The advent of deep Learning, a subset of machine learning, has revolutionised this field by offering greater accuracy. Therefore, deep learning has the potential to greatly enhance the accuracy of plant disease detection and classification. Recent research progress on the use of deep learning technology in the identification of crop leaf diseases is reviewed in this article. The current trends and challenges in plant leaf disease detection using advanced imaging techniques and deep learning are presented. This survey aims to provide a valuable resource for the researchers investigating the detection of plant diseases and detection of those using state of the art models for ease of saving time and cost. Additionally, the article also addresses some of the current challenges and issues in the detection process that need to be resolved.
A study on real time plant disease diagonsis systemIJARIIT
The document discusses developing a real-time plant disease diagnosis mobile application. It aims to allow farmers to easily capture images of plant leaves using a mobile camera, send the images to a central system for analysis, and receive diagnoses and treatment recommendations. The proposed system would use image processing and data mining techniques to analyze leaf images for abnormalities, identify the plant species, recognize any diseases present, and recommend appropriate pesticides and estimate treatment costs. This would provide a low-cost, convenient solution for farmers to quickly diagnose and respond to plant diseases.
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
This document discusses a proposed loss-fused convolutional neural network model for identifying and classifying plant disease. The model aims to improve predictive performance by combining the advantages of two different loss functions. The model was tested on a dataset from the Plant Village Database and achieved 98.93% accuracy in discriminating between affected and unaffected plant leaf samples, outperforming other existing methodologies. The paper provides background on plant disease detection techniques and reviews related work applying machine learning and deep learning methods.
Food is one of the basic needs of human being. Population is increasing day by day. So, it has become important to grow sufficient amount of crops to feed such a huge population. Agricultural intervention in the livelihood of rural India is about 58%. But with the time passing by, plants are being affected with many kinds of diseases, which cause great harm to the agricultural plant productions. It is very difficult to monitor the plant diseases. It requires tremendous amount of work, expertise in the plant diseases, and also require the excessive processing speed and time. Hence, image processing is used for the detection of plant diseases by just capturing the images of the leaves and comparing it with the data sets available. Latest and fostering technologies like Image processing is used to rectify such issues very effectively. In this project, four consecutive stages are used to discover the type of disease. The four stages include pre-processing, leaf segmentation, feature extraction and classification. This paper aims to support and help the farmers in an efficient way.
IRJET - Paddy Crop Disease Detection using Deep Learning Technique by using D...IRJET Journal
This document describes a study that uses deep learning and digital image processing techniques to detect diseases in paddy crops. The proposed system takes digital images of affected leaf parts as input and outputs the name of the detected disease and recommended pesticides. It first preprocesses and enhances the images before using edge detection, segmentation, and classification algorithms to analyze leaf parameters and identify diseases. The goal is to enable accurate, automated disease detection to help farmers protect crops and minimize pesticide usage.
This document presents a method for recognizing five fungal diseases of wheat (leaf rust, stem rust, yellow rust, powdery mildew, and septoria) using deep learning on images. A dataset of 2414 wheat disease images (WFD2020) was created with over 80% representing single diseases and over 12% healthy plants. A convolutional neural network with the EfficientNet architecture achieved the best accuracy of 94.2% for identification. The recognition method was implemented as a Telegram bot, allowing users to assess wheat plants for disease in field conditions using mobile devices.
Plant Disease Detection and Identification using Leaf Images using deep learningIRJET Journal
The document discusses a method for plant disease detection and identification using deep learning on leaf images. The method involves collecting images of plant leaves, preprocessing the images through steps like segmentation and grayscale conversion, and classifying plant diseases using a convolutional neural network (CNN) classifier. The goal is to develop an automated system for early and accurate detection of plant diseases to improve crop productivity and help farmers. The system could help reduce costs and time compared to traditional expert-based identification methods. Experimental results found the CNN achieved over 99% accuracy in identifying three common pomegranate diseases from leaf images.
Survey on Plant Disease Detection using Deep Learning based FrameworksAI Publications
Early identification of plant diseases is crucial as they can hinder the growth of their respective species. Although many machine learning models have been utilised for detecting and classifying plant diseases. The advent of deep Learning, a subset of machine learning, has revolutionised this field by offering greater accuracy. Therefore, deep learning has the potential to greatly enhance the accuracy of plant disease detection and classification. Recent research progress on the use of deep learning technology in the identification of crop leaf diseases is reviewed in this article. The current trends and challenges in plant leaf disease detection using advanced imaging techniques and deep learning are presented. This survey aims to provide a valuable resource for the researchers investigating the detection of plant diseases and detection of those using state of the art models for ease of saving time and cost. Additionally, the article also addresses some of the current challenges and issues in the detection process that need to be resolved.
A study on real time plant disease diagonsis systemIJARIIT
The document discusses developing a real-time plant disease diagnosis mobile application. It aims to allow farmers to easily capture images of plant leaves using a mobile camera, send the images to a central system for analysis, and receive diagnoses and treatment recommendations. The proposed system would use image processing and data mining techniques to analyze leaf images for abnormalities, identify the plant species, recognize any diseases present, and recommend appropriate pesticides and estimate treatment costs. This would provide a low-cost, convenient solution for farmers to quickly diagnose and respond to plant diseases.
Tomato Disease Fusion and Classification using Deep LearningIJCI JOURNAL
Tomato plants' susceptibility to diseases imperils agricultural yields. About 30% of the total crop loss is attributable to plants with disease. Detecting such illnesses in the plant is crucial to avoid significant output losses.This study introduces "data fusion" to enhance disease classification by amalgamating distinct disease-specific traits from leaf halves. Data fusion generates synthetic samples, fortifying a TensorFlow Keras deep learning model using a diverse tomato leaf image dataset. Results illuminate the augmented model's efficacy, particularly for diseases marked by overlapping traits. Enhanced disease recognition accuracy and insights into disease interactions transpire. Evaluation metrics (accuracy 0.95, precision 0.58, recall 0.50, F1 score 0.51) spotlight balanced performance. While attaining commendable accuracy, the intricate precision-recall interplay beckons further examination. In conclusion, data fusion emerges as a promising avenue for refining disease classification, effectively addressing challenges rooted in trait overlap. The integration of TensorFlow Keras underscores the potential for enhancing agricultural practices. Sustained endeavours toward enhanced recall remain pivotal, charting a trajectory for future advancements.
Optimized deep learning-based dual segmentation framework for diagnosing heal...IAESIJAI
The high disease prevalence in apple farms results in decreased yield and income. This research addresses these issues by integrating internet of things (IoT) applications and deep neural networks to automate disease detection. Existing methods often suffer from high false positives and lack global image similarity. This study proposes a conceptual framework using IoT visual sensors to mitigate apple diseases' severity and presents an intelligent disease detection system. The system employs the augmented Otsu technique for region-aware segmentation and a colour-conversion algorithm for generating feature maps. These maps are input into U-net models, optimized using a genetic algorithm, which results in the generation of suitable masks for all input leaf images. The obtained masks are then used as feature maps to train the convolution neural network (CNN) model for detecting and classifying leaf diseases. Experimental outcomes and comparative assessments demonstrate the proposed scheme's practical utility, yielding high accuracy and low false-positive results in multiclass disease detection tasks.
RICE PLANT DISEASE DETECTION AND REMEDIES RECOMMENDATION USING MACHINE LEARNINGIRJET Journal
This document describes a machine learning approach to detect diseases in rice plants from images and recommend remedies. It discusses three common rice diseases - leaf blast, bacterial leaf blight, and hispa - and how a convolutional neural network was trained on thousands of images to classify diseases. The proposed method uses CNN layers to extract features from images and fully connected layers to classify diseases. It aims to help farmers early detect diseases from photos and provide effective treatment recommendations to improve crop yields.
Bacterial foraging optimization based adaptive neuro fuzzy inference system IJECEIAES
Life of human being and animals depend on the environment which is surrounded by plants. Like human beings, plants also suffer from lot of diseases. Plant gets affected by completely including leaf, stem, root, fruit and flower; this affects the normal growth of the plant. Manual identification and diagnosis of plant diseases is very difficult. This method is costly as well as time-consuming so it is inefficient to be highly specific. Plant pathology deals with the progress in developing classification of plant diseases and their identification. This work clarifies the identification of plant diseases using leaf images caused by bacteria, viruses and fungus. By this method it can be identified and control the diseases. To identify the plant leaf disease Adaptive Neuro Fuzzy Inference System (ANFIS) was proposed. The proposed method shows more refined results than the existing works.
The document proposes an image processing methodology to automatically grade disease on pomegranate leaves and identify bacterial blight disease. It involves image acquisition, pre-processing, color segmentation using k-means clustering to extract diseased spots, and calculating disease and total leaf areas to determine the percent infection and corresponding disease grade. The system can identify bacterial blight by checking leaves for yellow margins around diseased areas and fruits for cracks passing through black spots. The results of automatic grading are more accurate and less time-consuming than manual grading.
International Journal of Research in Advent Technology (IJRAT),
VOLUME-7 ISSUE-11, NOVEMBER 2019,
ISSN: 2321-9637 (Online),
Published By: MG Aricent Pvt Ltd
IRJET- Disease Detection in the Leaves of Multiple PlantsIRJET Journal
1. The document proposes a deep learning-based method to detect diseases affecting the leaves of multiple plant varieties.
2. A convolutional neural network (CNN) model called ResNet-50 is trained on 1,222 images of diseased plant leaves representing 5 different diseases.
3. The CNN model achieves over 96% classification accuracy, outperforming conventional machine learning techniques for disease detection.
Deep learning for Precision farming: Detection of disease in plantsIRJET Journal
This document presents a method for detecting plant leaf diseases using deep learning and image processing techniques. The method uses the AlexNet convolutional neural network model to analyze images of leaves from a dataset. The images are preprocessed, augmented, and classified by AlexNet to identify different leaf diseases. A graphical user interface is also proposed to provide preventative measures for the detected leaf diseases. The study aims to help farmers identify diseases early to minimize crop loss and improve agricultural efficiency through automatic disease detection.
Leaf Disease Detection and Selection of Fertilizers using Artificial Neural N...IRJET Journal
This document proposes a system for detecting leaf diseases and selecting appropriate fertilizers using artificial neural networks. The system involves image acquisition, preprocessing, feature extraction using gray level co-occurrence matrix (GLCM) and statistical moments, training an artificial neural network, classifying diseases, and identifying the disease name and recommended fertilizer. It is intended to provide farmers with preventative treatment recommendations. The system is tested on mango and lemon leaves with two diseases each. If implemented, it could help farmers identify diseases early and apply the correct fertilizers to improve crop quality and yields.
Image based anthracnose and red-rust leaf disease detection using deep learningTELKOMNIKA JOURNAL
Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease’s detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and tranning of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from “ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101” show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease’s detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front.
IRJET- Semi-Automatic Leaf Disease Detection and Classification System for So...IRJET Journal
This document presents a semi-automatic system for detecting and classifying leaf diseases in soybean plants. The system uses image processing and machine learning techniques. It first segments leaf images into clusters using k-means clustering. It then extracts color and texture features from the clusters. Support vector machines are used to classify leaves as healthy or diseased, and to further classify diseased leaves into categories like downy mildew or leaf blight. The system achieves acceptable average accuracy levels that are better than existing methods. It provides a way to identify leaf diseases early in an automated manner to improve crop yields and food security.
An Innovative Approach for Tomato Leaf Disease Identification and its Benefic...IRJET Journal
This document summarizes an innovative approach for identifying diseases in tomato leaves using image processing and machine learning techniques. Specifically, a Convolutional Neural Network (CNN) model is developed and trained on a dataset of tomato leaf images showing various disease symptoms. Through testing and validation, the proposed approach achieves high accuracy in classifying different types of tomato leaf diseases. Integrating this method could enable timely disease detection, reduce crop losses, and optimize resource allocation for more sustainable agricultural practices. The research contributes a practical solution for automating tomato leaf disease detection to enhance disease management and food security.
This document describes a research project on crop disease detection using image processing and machine learning. The authors aim to develop a system that can recognize plant diseases from images of leaves by analyzing color, texture, and shape. The system would classify diseases using algorithms like convolutional neural networks (CNN), artificial neural networks (ANN), support vector machines (SVM), and fuzzy logic. This automatic disease detection could help farmers identify issues early and apply the proper treatments to prevent crop destruction and financial losses. The methodology captures leaf images and uses machine learning models trained on symptom features to diagnose common diseases like early rot and bacterial spots. The goal is to provide farmers with a fast and accurate disease identification tool.
Crop Leaf Disease Diagnosis using Convolutional Neural Networkijtsrd
This document summarizes a research paper that proposes a system for detecting crop leaf diseases using convolutional neural networks. The system can identify diseases in five major crops: corn, sugarcane, wheat, grape, and rice. It uses a MobileNet CNN model trained on a dataset of leaf images. Experiments show the system can accurately classify leaf diseases with 97.33% precision. The system automatically diagnoses leaf diseases and recommends pesticides, helping farmers detect and address issues early.
IRJET- Detection of Leaf Diseases and Classifying them using Multiclass SVMIRJET Journal
This document presents a method for detecting and classifying leaf diseases using image processing techniques. The proposed method involves image acquisition, preprocessing using median filtering, segmentation using k-means clustering, feature extraction of texture features using GLCM, and classification using multiclass SVM. Median filtering is used for noise removal before segmentation. K-means clustering segments the leaf from the image. GLCM extracts statistical texture features from the segmented leaf images. These features are then classified using multiclass SVM to identify the disease, achieving an accuracy of 97%. The method provides a fast and accurate way to detect leaf diseases using digital image processing and machine learning techniques.
Plant Diseases Prediction Using Image ProcessingIRJET Journal
This document discusses a system for predicting plant diseases using image processing. Specifically, it focuses on predicting diseases that affect tomato plant leaves. The system uses convolutional neural network techniques to analyze images of tomato leaves and predict whether they have diseases like Late blight, bacterial, or viral infections. It discusses implementing various steps like pre-processing images, feature extraction, and using a CNN classifier to classify images as having a specific disease or being healthy. The goal is to help farmers quickly and accurately identify plant diseases from leaf images to improve crop management and reduce economic losses.
IRJET- Agricultural Seed Disease Detection using Image Processing TechniqueIRJET Journal
This document discusses using image processing techniques to detect and classify seed diseases. It proposes a method using SVM classification on seed images to identify diseases and determine the extent of infection. Key steps include color space transformation, segmentation, feature extraction of texture properties, and classification. The method was able to accurately classify sample seed images as healthy, diseased or poisonous. If implemented, this technique could help farmers identify disease problems and obtain higher quality seeds for stronger crop yields.
Plant Leaf Recognition Using Machine Learning: A ReviewIRJET Journal
This document reviews various machine learning and deep learning algorithms that have been used for plant leaf recognition and classification. It summarizes 11 academic papers describing methods using support vector machines, convolutional neural networks, random forests, K-nearest neighbors, and other algorithms to classify plant species from leaf images with different levels of accuracy. The document concludes that these papers have suggested techniques to optimize accuracy, including preprocessing, feature extraction, and algorithm optimization, but that deep learning techniques like VGGNet often achieve the highest accuracy rates.
This document presents a proposed system for disease diagnosis of mango leaves using image processing techniques. The system uses a three-step process: 1) Image analysis to preprocess leaf images and extract affected regions, 2) Feature extraction of color and texture characteristics from affected regions, 3) Classification of leaf diseases based on extracted features using a trained machine learning model. The proposed system is intended to help agricultural specialists and farmers diagnose leaf diseases early and accurately by analyzing digital images of affected leaves. Some key advantages of the system include being low-cost, time-efficient, and able to diagnose multiple diseases. The system was tested on a dataset of 129 mango leaf images with promising 89.92% accuracy in identifying three common mango diseases.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
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Life of human being and animals depend on the environment which is surrounded by plants. Like human beings, plants also suffer from lot of diseases. Plant gets affected by completely including leaf, stem, root, fruit and flower; this affects the normal growth of the plant. Manual identification and diagnosis of plant diseases is very difficult. This method is costly as well as time-consuming so it is inefficient to be highly specific. Plant pathology deals with the progress in developing classification of plant diseases and their identification. This work clarifies the identification of plant diseases using leaf images caused by bacteria, viruses and fungus. By this method it can be identified and control the diseases. To identify the plant leaf disease Adaptive Neuro Fuzzy Inference System (ANFIS) was proposed. The proposed method shows more refined results than the existing works.
The document proposes an image processing methodology to automatically grade disease on pomegranate leaves and identify bacterial blight disease. It involves image acquisition, pre-processing, color segmentation using k-means clustering to extract diseased spots, and calculating disease and total leaf areas to determine the percent infection and corresponding disease grade. The system can identify bacterial blight by checking leaves for yellow margins around diseased areas and fruits for cracks passing through black spots. The results of automatic grading are more accurate and less time-consuming than manual grading.
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VOLUME-7 ISSUE-11, NOVEMBER 2019,
ISSN: 2321-9637 (Online),
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1. The document proposes a deep learning-based method to detect diseases affecting the leaves of multiple plant varieties.
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IRJET- Detection of Leaf Diseases and Classifying them using Multiclass SVMIRJET Journal
This document presents a method for detecting and classifying leaf diseases using image processing techniques. The proposed method involves image acquisition, preprocessing using median filtering, segmentation using k-means clustering, feature extraction of texture features using GLCM, and classification using multiclass SVM. Median filtering is used for noise removal before segmentation. K-means clustering segments the leaf from the image. GLCM extracts statistical texture features from the segmented leaf images. These features are then classified using multiclass SVM to identify the disease, achieving an accuracy of 97%. The method provides a fast and accurate way to detect leaf diseases using digital image processing and machine learning techniques.
Plant Diseases Prediction Using Image ProcessingIRJET Journal
This document discusses a system for predicting plant diseases using image processing. Specifically, it focuses on predicting diseases that affect tomato plant leaves. The system uses convolutional neural network techniques to analyze images of tomato leaves and predict whether they have diseases like Late blight, bacterial, or viral infections. It discusses implementing various steps like pre-processing images, feature extraction, and using a CNN classifier to classify images as having a specific disease or being healthy. The goal is to help farmers quickly and accurately identify plant diseases from leaf images to improve crop management and reduce economic losses.
IRJET- Agricultural Seed Disease Detection using Image Processing TechniqueIRJET Journal
This document discusses using image processing techniques to detect and classify seed diseases. It proposes a method using SVM classification on seed images to identify diseases and determine the extent of infection. Key steps include color space transformation, segmentation, feature extraction of texture properties, and classification. The method was able to accurately classify sample seed images as healthy, diseased or poisonous. If implemented, this technique could help farmers identify disease problems and obtain higher quality seeds for stronger crop yields.
Plant Leaf Recognition Using Machine Learning: A ReviewIRJET Journal
This document reviews various machine learning and deep learning algorithms that have been used for plant leaf recognition and classification. It summarizes 11 academic papers describing methods using support vector machines, convolutional neural networks, random forests, K-nearest neighbors, and other algorithms to classify plant species from leaf images with different levels of accuracy. The document concludes that these papers have suggested techniques to optimize accuracy, including preprocessing, feature extraction, and algorithm optimization, but that deep learning techniques like VGGNet often achieve the highest accuracy rates.
This document presents a proposed system for disease diagnosis of mango leaves using image processing techniques. The system uses a three-step process: 1) Image analysis to preprocess leaf images and extract affected regions, 2) Feature extraction of color and texture characteristics from affected regions, 3) Classification of leaf diseases based on extracted features using a trained machine learning model. The proposed system is intended to help agricultural specialists and farmers diagnose leaf diseases early and accurately by analyzing digital images of affected leaves. Some key advantages of the system include being low-cost, time-efficient, and able to diagnose multiple diseases. The system was tested on a dataset of 129 mango leaf images with promising 89.92% accuracy in identifying three common mango diseases.
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Insights on assessing image processing approaches towards health status of plant leaf using machine learning
1. IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 12, No. 2, June 2023, pp. 884~891
ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i2.pp884-891 884
Journal homepage: http://ijai.iaescore.com
Insights on assessing image processing approaches towards
health status of plant leaf using machine learning
Harsha Raju, Veena Kalludi Narasimhaiah
School of Electronics and Communication Engineering, Reva University, Bengaluru, India
Article Info ABSTRACT
Article history:
Received Jul 3, 2022
Revised Sep 22, 2022
Accepted Oct 22, 2022
With the advancement of digital image processing in agriculture and crop
cultivation, imaging techniques are adopted to acquire real-time health
status. Out of all the parts of plants, the leaf is the direct indicator of its
health status, and hence applying various image processing approaches could
benefit the process of yielding informative cases of plant health. At present,
there are various approaches, e.g., feature extraction, segmentation,
identification, the classification being evolved up with more dependencies
being found in using machine learning; the studies show many contributions
towards this challenge. However, it is not yet conclusive to understand the
optimal approach. Hence, this paper highlights an explicit strength and
weakness associated with the existing approaches existing imaging
processing techniques to identify the disease condition from an input of plant
leaves' image. The study also contributes to highlighting open-end research
problems to have conclusive remarks about effectiveness.
Keywords:
Classification
Disease
Identification
Image processing
Machine learning
Plant leaves
This is an open access article under the CC BY-SA license.
Corresponding Author:
Harsha Raju
School of Electronics and Communication Engineering, Reva University
Bengaluru, India
Email: harsh4no1@gmail.com
1. INTRODUCTION
With the increasing population score, there is an increasing demand for quality nutrition from a
higher grade of food quality. This can only be confirmed when the cultivation is done because a healthy
atmosphere and crops offer higher quality grains. However, diseases in the plants are inevitable, and it offers
potential degradation towards the quality of the grains. Therefore, there is a higher degree of concern towards
the diseases that inflict plants resulting in minimal production and yield. Such infliction can occur in any
plants, right from roots, stems, branches, buds, flowers and leaves. There could be multiple reasons for this
viz. adverse climatic condition, degraded quality of soil, poor irrigation, inferior practices in farming and
adoption of conventional methods. With the increasing technology usage towards agriculture and cultivation,
there are more chances of higher and quality yield in plants [1]–[5]. Sensors can be deployed over the
cultivation fields to extract various data associated with the plants [6], [7]. Sensors can capture the images of
plants that can be transmitted to another end, where an image processing algorithm can be executed to find
the real-time status of the plant’s health. It can be said that the majority of the diseases that have a negative
impact on crop yield are highly visible, and this can be an identifier for image processing algorithms. Hence,
an algorithm can be constructed based on formulated identifiers matching the specific information about the
disease. A human can also assess such visual information in identifying the disease condition in plants.
However, a human cannot monitor this abnormality for the crop field of a larger dimension. Therefore, there
is a need for an automated approach that can prevent human interaction from carrying out this task of
identification. As most of the problems associated with the disease are visually seen; therefore, this
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information can be checked autonomously by the machine itself using image processing. However, there are
various challenges involved in this process. The first challenge is to perform extraction of features from
many crops, and hence feature extraction is one essential operation [8]. There are various feature extraction
mechanisms using various features viz. shape, energy, entropy, contrast, texture, fractal dimension, variation
in red green blue (RGB) color, descriptors, grey levels histograms. Extensive work has been carried out
towards this in the existing system. These extracted features are then used for further processing in order to
carry out identification. In this process, two more processes evolve viz. identification and classification. The
identification process generally attempts to match the defined data of disease with the input image, while
classification generally implements a machine learning approach [9], [10] to categorize the types of disease.
However, all these approaches are used for disease detection in plant leaf images and are also deployed for
other purposes.
This paper offers a snapshot of effectiveness in existing approaches towards applying computer
vision over-identifying abnormalities in crop cultivation. This paper's organization is: section 2 discusses the
diseases in plant leaves, followed by a discussion about existing approaches in section 3. This section further
discusses all essential approaches and highlights their strength and weakness. Briefing of the existing trend of
research is shown in section 4. Section 5 discusses the open-end issues about the existing studies while
summarizing this paper in section 6.
Diseases in plant leave, before implying the image processing domain over the plant leaf's images, it
is essential to understand the logical information about the disease associated with plants. Table 1 highlights
some of the frequently studied diseases in plants from their leaf concerning disease representation, its
corresponding pathogens, color space, and classifier used to categorize it. Hence, the infection in plants can
occur by various means, and the leaf is one of the prominent areas of its diagnosis. The information stated in
Table 1 shows various ways to analyze the disease and identify various pathogens. Understanding this fact is
essential in order to develop a sufficient identification and classification approach. Such diseases can occur
due to various reasons viz. nutrient deficiency, fungus, insect, and bacteria as shown in Figure 1. This
information assists in developing a better classification process.
Table 1. Existing methods
Plant type Representation of disease Pathogen Color space Classification
Pea/bean [11] Changes in the color of a leaf Deficiency of nutrient HIS Spatial (Euclidean)
Maize [12] Color of the leaf, changes in
morphology
Shealth blight, leaf
blight
YCbCr Neural network (Backpropagation)
Rice [13] -do- Deficiency of nutrient RGB Multilayer perceptron
Grapes [14] Color of leaf, pigmentation Pest RGB Neural network
Cotton [15] Strikes, Stains, spots Bacteria, bug hue,
saturation,
value (HSV),
RGB
Support vector machine
Soyabean [16] Spot in leaf Fungus HIS The ratio of a spot with the lesion to
the area of leaf
Maize [17] Chlorotic area Maize streak virus Greyscale Thresholding of Pixel
Maize [18] Holes in leaf Fall armyworm Greyscale Thresholding
Reasons for Diseases in Plant
Leaves
Fungus
Nutrient
Deficiency
Insect Bacteria
Figure 1. Factors for infection in plants
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The bacterial infection is identified from various external visuals, e.g., brown spot, soft rot, blight,
Myrothecium and mould. The fungal infection identification can be found by leaf spot, red spot, stripe, rust,
black spot and scab. However, it is not the same for nutrient deficiency as such effect may be visually seen in
leaves, and sometimes it may be internal. Nutrient deficiency affects the complete plant and is much adverse
compared to other diseases found from leaves. The diseases caused due to insects can be visually identified
over leaves. All this information is technically required to formulate the criteria of detection and
classification in plant leaves. Out of all the different approaches, the existing approaches are mainly found to
tend to adopt machine learning approaches, especially deep learning techniques. There are specific reasons
for this viz. i) adoption of deep learning allows effective handling of the data in the presence of a higher
degree of noises; ii) adoption of deep learning also led to higher accuracy over the test images and it tends to
reduce the dependency over a greater number of the test image; iii) identification criteria of complex types
can be well formulated in in-depth learning approach; and iv) deep learning also led to an evolution of a
predictive approach. Some studies have used the statistical inference approach towards disease identification
over plant leaves.
Therefore, there are various approaches at present which is used for the identification of the diseases
from plant leaves. While some processes address one specific technique and other amalgamate multiple
existing approaches to achieve the objective of disease identification from plant leaves. The next section
discusses the researchers' contribution more vividly in recent times associated with implying digital image
processing over plant leaves.
At present, various work is being carried out towards investigating the specific problems associated
with plant leaves using image processing. For this purpose, a thorough check is carried out towards various
reputed journals to find all the signification processing techniques applied over plant leaves as shown in
Table 1. It was found that such forms of investigation are carried out for two purposes viz. for disease
identification and for other purposes, which is application-specific. All the journals published within the last
decade have been considered for the review process. The prime target is to understand the strength and
weaknesses of image processing approaches towards plant leaves as a case study. Following is the briefing of
approaches.
The first set of the approach is related to segmentation, which is used for differentiating foreground
object from the background scene. This process is utilized for localizing the object for a given scene. A
unique approach of automated segmentation is introduced by Janssens et al. [19], where the system segment
leaves from plants and obtains information about the leaf's symmetry line. The disease factor can be
identified from this line of symmetry. This work's contribution is to use a unique feature extraction where
image moments are extracted based on contours. The technique is found to offer a parallel process of
multiple images at the same time. Another essential segmentation approach is introduced by Sun et al. [20],
where multiple regression of linear form is used.
The second approach is related to feature extraction, which obtains numerical features from the raw
image, making the image suitable for further processing without losing any significant information. Another
benefit of performing feature extraction is dimensional reduction. The process of feature extraction has been
carried out over plant leave where essential disease spot is identified in the process. The work carried out by
Li et al. [21] has discussed histogram-based segmentation using an evolutionary approach where the gray
portion of the leaves represents disease spot. The study has used statistical and visual features for spotting the
lesion location. The technique uses a genetic algorithm to carry out segmentation; however, increasing the
number of images will also degrade the search efficiency.
Moreover, this work does not support persistent feature extraction if the environment is dynamic.
This problem is addressed in Lv et al. [22], where maize features are extracted in a complex environment
using a neural network. The issues associated with the overfitting of a neural network are addressed using
batch normalization. A similar direction of work is also carried out by Sun et al. [23], where deep learning is
used along with the fusion of multi-scale form features. This mechanism also carries out preprocessing over
the dataset, where the Retinex-based approach is used. The study performs fusing of low- and high-level
features in order to accomplish better accuracy performance.
The third and most frequently used approach is the classification method towards identifying the
disease in a plant leaf. It has been seen that machine learning is the dominant method for addressing
classification related issues. The implication of machine learning was reported to identify the leaf rust
disease, as discussed in the work of Ashourloo et al. [24]. The technique evaluates the spectra of leaf image
using electromagnetic region where multiple machine learning approach has been assessed viz. gaussian
regression process, support vector regression, and regression using partial least square. The study outcome
shows the robustness of the machine learning approach. Although this technique offers robustness in the
learning mechanism, it still induces higher memory consumption, increasing training operation over different
forms of images. Hence, a better version of optimization is required to improve the classification
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performance further. A study to achieve this goal is reported to be accomplished using a bacterial foraging
algorithm when integrated with the frequently used neural network with radial basis function. This work is
discussed by Chouhan et al. [25], where bacterial foraging is used for allocating enhanced weight towards the
radial basis function. This phenomenon is proven to increase accuracy. The study has considered the
identification and classification of fungal disease on the leaf where higher accuracy performance is noted.
However, the study is carried out over pre-trained images whose impact relationship with image quality
cannot be identified. This issue is considered in Dai et al. [26], where it is stated that a fusion-based approach
with a generative network could yield better results.
The idea of this study is also to obtain an image with a higher resolution. This technique's positive
point is its ability to detect multiple diseases, but its dependencies on the texture information are not found
within the dataset. The prime reason for this is the non-inclusion of vegetation indices. Even this index is
considered lower than its computational capability to identify variations in features in different diseases. This
problem is addressed by Huang et al. [27], where vegetation indices have been substituted by spectral indices
obtained from wheat's hyperspectral image. The study obtains weight information from bands of highly
correlated wavelength in order to generate spectral indices. The outcome shows better detection performance.
Adoption of hyperspectral images and disease detection is also seen in the work of Moriya et al. [28],
considering the case study of sugarcane plants. The study develops a library that consists of hyperspectral
image of both healthy and unhealthy plants. The technique further uses the block of the radiometer to obtain
specific features of reflectance for generating mosaic for identification. Further kappa statistics are used for
the classification process. The work carried out by Jiang et al. [29] have considered the case study of the
identification of disease in apple leaves using enhanced convolution neural network on a real-time basis.
To some extent, such adoption of a rule-based approach offers better optimization enhancement in
handling such issues. The work in this direction has been noted by Kaur et al. [30], where a rule-based
approach is used for performing classification operation considering the case study of the classification of
leave disease in the soybean plant. Using the k-mean algorithm, the study uses multiple forms of features
(e.g., texture and color.) where the training operation is carried out using a support vector machine. Pham et
al. [31] carried out a study towards the early classification process, emphasizing the heuristic-based solution
using a hybrid approach. The idea is to implement a forward feed network to identify small diseases in plants.
The study also performs contrast enhancement as a preprocessing step followed by segmenting blob. The
neural network takes the input of the feature to carry out training and later the classification. The adoption of
deep learning is also seen in Wang et al. [32], where the automated process is used to assess the severity of
the disease in plants. This classification approach has used segmentation using thresholding and used feature
engineering. A similar approach to detecting severity is used by Zeng et al. [33] using deep learning. The
comparesion of strength and weakness for different authors is as shown in Table 2.
Table 2. Summary of the effectiveness of existing approache
Authors Problems Strength Weakness
Janssens et al. [19] Segmentation Parallel processing Uses high-end processor to get results
Sun et al. [20] Segmentation Reliability, higher precision -Accuracy can be furthermore optimized
-Uses high-end processor to get results
Li et al. [21] Feature extraction Enhance the efficiency of search Does not support scalable performance
Lv et al. [22] Feature extraction Optimal learning process Specific to disease
Sun et al. [23] Feature extraction Higher precision Specific to disease
Ashourloo et al. [24] Classification Robustness learning method Could induce computational complexity
2. RESEARCH TRENDS
Understanding the research trend is essential to visualize the direction of the ongoing research in the
area of analyzing disease from the plant leaves. For this purpose, all the research papers that are published
during 2010-2020 are collected from reputed publication and reviewed. The analysis shows various explicit
trends which are discussed in this section.
3. ANALYSIS OF EXISTING METHODS
At present, there are many existing methods that has been formed in order to address the issues
associated with applying digital image processing over plant leaves. An outcome shown in Figure 2 shows
that majority of the work has been carried out towards feature extraction process. Hence, the emphasis is
more offered toward feature extraction process as essential information obtained from the extracted feature is
highly contributory towards detection and classification. The adoption of deep learning, neural network, and
support vector machines are next trend observed. This eventually means that more work is carried out
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towards classification process using machine learning. However, segmentation being an important part of
image processing has seen a smaller number of attention and similar trend is found for regression. The rule-
based method of fuzzy logic is also found to be very less adopted in existing approaches. Hence, the outcome
of this inference is more usage of feature extraction methods and machine learning are frequently adopted
topic of research in present era.
Figure 2. Trends towards existing methods
4. ANALYSIS OF ACCURACY ACHIEVED
Accuracy is one of the essential parameters for identification as well as classification. Exploring the
research trend toward accuracy is essential to understand the achievement level, signifying the strength of
existing approaches. Figure 3 showcase that means of the accuracies achieved from all research work
published between 2011 to 2020. A closer look into the trend shows a steep fall of accuracy from 2011 to
2014 where the researchers have focused on different sets of problems where accuracy is considered a
secondary parameter of performance. The different sets of problems include addressing testing with multiple
forms of images, checking the visual quality, and considering parameters of training and run time of the
algorithm. However, the accuracies started increasing from 2015 onwards stating that researchers are
prioritizing the accuracy as the essential parameters of their experiments. The graphical outcome of trend also
shows that accomplishment of accuracy remains nearly linear during 2018-2020.
Figure 3. Trends towards accuracy obtained
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5. ANALYSIS OF SCALABILITY
A scalability is assessed by evaluating the score of accuracy over increasing number of images
dataset. It basically shows how a uniform algorithm could offer consistency of accuracy when exposed to
different forms of images. The outcome shown in Figure 4 highlights that there are 55% of consistency in
accuracy when exposed to increasing number of different forms of images. Therefore, it can be said that
existing approaches are required to work more towards increasing this scalability factor and they are yet nor
ready to be deployed for commercial application which demands much better consistency in accuracy.
Hence, there is a need of more experiments and a greater number of modellings to evolve up with a new
solution towards working on this scalability factor of accuracy.
Figure 4. Trends towards accuracy concerning images
6. OPEN END RESEARCH PROBLEMS
Open-end research problems: There are very few works being carried out towards preprocessing the
input of plant leaf images. The preprocessing challenges in analyzing the plant's disease by the computer
vision systems face some unique challenges compared to other image processing tasks of preprocessing.
These challenges include viz. i) the occlusion on the leaves in the original images contain overlapping
conditions that are a constitute of the noise; ii) the contrast mismatch and its balance among the fruit, its
leaves, and background require efficient adjustment; iii) the highly varying lighting condition in the real-time
scenario as the weather and sun variation brings associated challenges as well as due to the density factor of
the orchard there exit low-intensity effect on the input image data; and iv) the imaging modality consists of
large numbers of correlated but redundant feature set as an outcome of the various wavelength bands imaging
modality.
Although studies are carried out in segmentation, it has not addressed the fundamental challenges
within it. The challenges during the process of separating the disease affected portion from the background of
the image include the following challenges: i) handling the dynamics of color associated with the disease
during the segmentation based on the color feature-set; ii) handling the dimension overhead due to large color
features of the original color of the fruits; and iii) variations of light condition, dimension or spread size of
the disease, volume of the fruits are another essential challenges to be considered while designing the
effective segmentation algorithms. Another important aspect towards the challenges to be handled during the
segmentation process is that the popular region growing algorithm consumes more time, so the time overhead
is not suitable to build the method suitable for the real-time process as well as consideration of the
localization of the diseases affected area, its geometric features and textures require effective descriptors and
extractor to get a better feature set for the learning model. Therefore, open-end research problems can be
summarized as:
a. The sensor and advance imaging systems pose enormous complexities for the deployments as they are
not cost-effective.
b. Many of the methods consider only the accuracy as performance parameters and do not consider metrics
like F1-Score to handle the trade-off between accuracy and precision.
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c. Much of the work in disease detection focuses on the classification part but significantly less work on
an integrated framework considering all aspects like preprocessing, segmentation, feature extraction,
and learning model to provide an overall better sensitivity and specificity results along with the
computation and time complexities trade-offs.
d. For the model validations, most models benchmark their work or optimize the model only for accuracy.
In contrast, apart from the specificity and sensitivity analysis, the focus should also minimize the
computational and time complexities to move the evolution towards real-time implementations.
e. The literature lacks significant optimization method inclusions.
7. CONCLUSION
This paper has discussed an essential approach used to identify the abnormalities in crop cultivation
using plant leaves' image. With a certain amount of strength in existing techniques of image processing, there
is a more significant number of challenges associated with it. Hence, our future work will be focused on
addressing the identified challenges and open-end research problems. The study will be carried out toward
developing an evaluation framework in order to offer better performance.
REFERENCES
[1] M. S. Farooq, S. Riaz, A. Abid, K. Abid, and M. A. Naeem, “A survey on the role of IoT in agriculture for the implementation of
smart farming,” IEEE Access, vol. 7, pp. 156237–156271, 2019, doi: 10.1109/ACCESS.2019.2949703.
[2] F. H. Tseng, H. H. Cho, and H. Te Wu, “Applying big data for intelligent agriculture-based crop selection analysis,” IEEE Access,
vol. 7, pp. 116965–116974, 2019, doi: 10.1109/ACCESS.2019.2935564.
[3] Z. Hu et al., “Application of non-orthogonal multiple access in wireless sensor networks for smart agriculture,” IEEE Access,
vol. 7, pp. 87582–87592, 2019, doi: 10.1109/ACCESS.2019.2924917.
[4] M. Ayaz, M. Ammad-Uddin, Z. Sharif, A. Mansour, and E. H. M. Aggoune, “Internet-of-things (IoT)-based smart agriculture:
Toward making the fields talk,” IEEE Access, vol. 7, pp. 129551–129583, 2019, doi: 10.1109/ACCESS.2019.2932609.
[5] J. Chen and A. Yang, “Intelligent agriculture and its key technologies based on internet of things architecture,” IEEE Access,
vol. 7, pp. 77134–77141, 2019, doi: 10.1109/ACCESS.2019.2921391.
[6] O. Kaiwartya et al., “T-MQM: testbed-based multi-metric quality measurement of sensor deployment for precision agriculture - a
case study,” IEEE Sensors Journal, vol. 16, no. 23, pp. 8649–8664, 2016, doi: 10.1109/JSEN.2016.2614748.
[7] D. W. Sambo, A. Forster, B. O. Yenke, I. Sarr, B. Gueye, and P. Dayang, “Wireless underground sensor networks path loss model
for precision agriculture (WUSN-PLM),” IEEE Sensors Journal, vol. 20, no. 10, pp. 5298–5313, 2020,
doi: 10.1109/JSEN.2020.2968351.
[8] H. Zhang et al., “Image classification using rapidEye data: integration of spectral and textual features in a random forest
classifier,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 12, pp. 5334–5349,
2017, doi: 10.1109/JSTARS.2017.2774807.
[9] U. Shafi et al., “A multi-modal approach for crop health mapping using low altitude remote sensing, internet of things (IoT) and
machine learning,” IEEE Access, vol. 8, pp. 112708–112724, 2020, doi: 10.1109/ACCESS.2020.3002948.
[10] M. K. Sott et al., “Precision techniques and agriculture 4.0 technologies to promote sustainability in the coffee sector: state of the
art, challenges and future trends,” IEEE Access, vol. 8, pp. 149854–149867, 2020, doi: 10.1109/ACCESS.2020.3016325.
[11] M. Wiwart, G. Fordoński, K. Zuk-Gołaszewska, and E. Suchowilska, “Early diagnostics of macronutrient deficiencies in three
legume species by color image analysis,” Computers and Electronics in Agriculture, vol. 65, no. 1, pp. 125–132, 2009,
doi: 10.1016/j.compag.2008.08.003.
[12] S. Kai, Z. Liu, H. Su, and C. Guo, “A research of maize disease image recognition of corn based on BP networks,” Proceedings -
3rd International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2011, vol. 1, pp. 246–249,
2011, doi: 10.1109/ICMTMA.2011.66.
[13] P. Sanyal, U. Bhattacharya, S. K. Parui, S. K. Bandyopadhyay, and S. Patel, “Color texture analysis of rice leaves diagnosing
deficiency in the balance of mineral levels towards improvement of crop productivity,” pp. 85–90, 2008,
doi: 10.1109/icit.2007.40.
[14] J. Lloret, I. Bosch, S. Sendra, and A. Serrano, “A wireless sensor network for vineyard monitoring that uses image processing,”
Sensors, vol. 11, no. 6, pp. 6165–6196, 2011, doi: 10.3390/s110606165.
[15] A. Camargo and J. S. Smith, “An image-processing based algorithm to automatically identify plant disease visual symptoms,”
Biosystems Engineering, vol. 102, no. 1, pp. 9–21, 2009, doi: 10.1016/j.biosystemseng.2008.09.030.
[16] W. Shen, Y. Wu, Z. Chen, and H. Wei, “Grading method of leaf spot disease based on image processing,” Proceedings -
International Conference on Computer Science and Software Engineering, CSSE 2008, vol. 6, pp. 491–494, 2008,
doi: 10.1109/CSSE.2008.1649.
[17] D. P. Martin and E. P. Rybicki, “Microcomputer-based quantification of maize streak virus symptoms in Zea mays,”
Phytopathology, vol. 88, no. 5, pp. 422–427, 1998, doi: 10.1094/PHYTO.1998.88.5.422.
[18] D. G. Sena, F. A. C. Pinto, D. M. Queiroz, and P. A. Viana, “Fall armyworm damaged maize plant identification using digital
images,” Biosystems Engineering, vol. 85, no. 4, pp. 449–454, 2003, doi: 10.1016/S1537-5110(03)00098-9.
[19] O. Janssens et al., “Leaf segmentation and parallel phenotyping for the analysis of gene networks in plants,” European Signal
Processing Conference, pp. 1–5, 2013.
[20] G. Sun, X. Jia, and T. Geng, “Plant diseases recognition based on image processing technology,” Journal of Electrical and
Computer Engineering, vol. 2018, 2018, doi: 10.1155/2018/6070129.
[21] K. Li, L. Xiong, D. Zhang, Z. Liang, and Y. Xue, “The research of disease spots extraction based on evolutionary algorithm,”
Journal of Optimization, vol. 2017, pp. 1–14, 2017, doi: 10.1155/2017/4093973.
[22] M. Lv, G. Zhou, M. He, A. Chen, W. Zhang, and Y. Hu, “Maize leaf disease identification based on feature enhancement and
DMS-robust Alexnet,” IEEE Access, vol. 8, pp. 57952–57966, Mar. 2020, doi: 10.1109/access.2020.2982443.
8. Int J Artif Intell ISSN: 2252-8938
Insights on assessing image processing approaches towards health status of plant leaf … (Harsha Raju)
891
[23] J. Sun, Y. Yang, X. He, and X. Wu, “Northern maize leaf blight detection under complex field environment based on deep
learning,” IEEE Access, vol. 8, pp. 33679–33688, 2020, doi: 10.1109/ACCESS.2020.2973658.
[24] D. Ashourloo, H. Aghighi, A. A. Matkan, M. R. Mobasheri, and A. M. Rad, “An investigation into machine learning regression
techniques for the leaf rust disease detection using hyperspectral measurement,” IEEE Journal of Selected Topics in Applied Earth
Observations and Remote Sensing, vol. 9, no. 9, pp. 4344–4351, 2016, doi: 10.1109/JSTARS.2016.2575360.
[25] S. S. Chouhan, A. Kaul, U. P. Singh, and S. Jain, “Bacterial foraging optimization based radial basis function neural network
(BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards plant pathology,” IEEE
Access, vol. 6, pp. 8852–8863, 2018, doi: 10.1109/ACCESS.2018.2800685.
[26] Q. Dai, X. Cheng, Y. Qiao, and Y. Zhang, “Crop leaf disease image super-resolution and identification with dual attention and
topology fusion generative adversarial network,” IEEE Access, vol. 8, pp. 55724–55735, 2020, doi: 10.1109/ACCESS.2020.2982055.
[27] W. Huang et al., “New optimized spectral indices for identifying and monitoring winter wheat diseases,” IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 2516–2524, 2014,
doi: 10.1109/JSTARS.2013.2294961.
[28] E. A. S. Moriya, N. N. Imai, A. M. G. Tommaselli, and G. T. Miyoshi, “Mapping mosaic virus in sugarcane based on
hyperspectral images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 2,
pp. 740–748, 2017, doi: 10.1109/JSTARS.2016.2635482.
[29] P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, “Real-time detection of apple leaf diseases using deep learning approach based on
improved convolutional neural networks,” IEEE Access, vol. 7, pp. 59069–59080, 2019, doi: 10.1109/ACCESS.2019.2914929.
[30] S. Kaur, S. Pandey, and S. Goel, “Semi-automatic leaf disease detection and classification system for soybean culture,” IET Image
Processing, vol. 12, no. 6, pp. 1038–1048, 2018, doi: 10.1049/iet-ipr.2017.0822.
[31] T. N. Pham, L. Van Tran, and S. V. T. Dao, “Early disease classification of mango leaves using feed-forward neural network and
hybrid metaheuristic feature selection,” IEEE Access, vol. 8, pp. 189960–189973, 2020, doi: 10.1109/ACCESS.2020.3031914.
[32] G. Wang, Y. Sun, and J. Wang, “Automatic image-based plant disease severity estimation using deep learning,” Computational
Intelligence and Neuroscience, vol. 2017, 2017, doi: 10.1155/2017/2917536.
[33] Q. Zeng, X. Ma, B. Cheng, E. Zhou, and W. Pang, “GANS-based data augmentation for citrus disease severity detection using
deep learning,” IEEE Access, vol. 8, pp. 172882–172891, 2020, doi: 10.1109/ACCESS.2020.3025196.
BIOGRAPHIES OF AUTHORS
Harsha Raju received his master degree in VLSI and Embedded systems.
Currently persuing his doctor of philosophy in the school of Electroniccs and
Communinaction Engineerin, REVA university. Harsha is currently working as an Assistant
Professor in Dr Ambedkar Institute of Technology Bengaluru. He has had many national and
international patents under his belt, out of which few patents are granted. He has published
many international journals which are indexed in scopus, google scholor and web of science
REVA University I would thank REVA university for providing me the oppourtunity for
persuing my Doctrate of philosophy. The infrastructure in the campus is just excellent which
makes any scholor to put in effort for persuing his education. He can be contacted at email:
harsh4no1@gmail.com.
Dr. Veena Kalludi Narasimhaiah received her Ph. D degree in wireless sensor
networs which was awarded by Kuvempu University under the guidance of Dr. B.P. Vijaya
Kumar, Professor and Head, Dept. of Information Science and Engg., MSRIT, Bangalore.
She is working as an Associate Professor in the school of Electronics and Communication
Engineering, Reva university. She has had around 50 international journals which are indexed
in scopus, google scholor and web of science. She was Qualified for GATE All India level
exam in 1999 and joined NITK Surathkal for M. Tech in Digital Electronics and Advanced
Communication course. She has had many national and international patents under his belt,
out of which few patents are granted. She can be contacted at email: veenakn@reva.edu.in.