This document discusses the development of an automated content-based image retrieval system for assessing crop ripeness. It proposes using computer vision techniques to classify images of crops, like tomatoes and bell peppers, into different ripeness stages. The system uses pre-processing like resizing, background removal and color space conversion. Features like color moments and histograms are extracted and reduced using PCA. A support vector machine classifier with different kernels is used for classification. Experimental results show using a linear kernel and increasing training data improved accuracy for tomato classification in the one-against-all approach. Both one-against-all and one-against-one multi-class SVMs with 10-fold cross-validation were later tested on 250 images of each crop.
The stage of maturity is utmost important for the harvesting of any crop. and for horticultural and plantation crops, its like the very life of those crops
Harvesting practices for special market purposePawan Nagar
1. Harvesting is the act of gathering a ripe crop from the fields using various manual and mechanical methods depending on the crop.
2. Maturity indices help determine the optimal time to harvest crops to ensure acceptable quality for consumers. Indices include factors like color, firmness, sugar and acid content, and days after flowering.
3. The presentation discusses various maturity indices for fruits like mango, banana, citrus and vegetables and the importance of harvesting at the proper maturity stage.
Lecture 1: Importance of Postharvest TechnologyKarl Obispo
The document discusses postharvest technology, including:
1. Defining postharvest technology and explaining its importance in preventing food losses, improving nutrition, adding value to agricultural products, and generating jobs.
2. The three main objectives of postharvest technology are maintaining quality, protecting food safety, and reducing losses between harvest and consumption.
3. Common causes of postharvest losses in the Philippines include rough handling, inadequate cooling and temperature control, lack of sorting, and inadequate packaging. Proper temperature management and reducing damage is key to reducing losses.
Lecture 3: Fruits and Vegetables HarvestingKarl Obispo
This document discusses harvesting of fruits and vegetables. It begins with learning objectives related to postharvest procedures, maturity indices, and harvesting practices. It then outlines topics to be covered including postharvest handling procedures, defining maturity indices, importance of maturity indices, differences between physiological and horticultural maturity, and harvesting practices for common fruits and vegetables. The document discusses factors that determine optimum maturity for harvesting, different types of maturity, maturity indices used for various fruits and vegetables, and methods for manual and mechanical harvesting. It stresses the importance of harvesting at proper maturity to ensure quality and storage life.
This document discusses fruit ripening conditions and factors that influence the ripening process. It describes how climacteric and non-climacteric fruits differ in their respiration rates during ripening. Ripening involves physical and chemical changes in the fruit brought about by ethylene exposure and environmental conditions like temperature and humidity. Key changes during ripening include softening of texture, changes in color, sugar content, acidity, and the development of flavor and aroma compounds. The role of ethylene and controlled atmosphere storage conditions in modulating the ripening process is also covered.
This document provides a technical guide book on banana cultivation in Pakistan. It begins with an acknowledgment and abstract. It then provides background information on the history of banana cultivation, major varieties grown in Pakistan, and the growth cycle of banana plants. It discusses the agronomic requirements for banana such as climate, soil, fertilizer and water needs. It outlines the major districts in Sindh province where banana is grown and production practices used there. It also covers pest and disease management, harvesting techniques, and recommendations to improve production and post-harvest management in Pakistan. The guide book aims to provide technical assistance to growers, exporters and others involved in the banana value chain in Pakistan.
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
Random forest is an ensemble machine learning algorithm that combines multiple decision trees to improve predictive accuracy. It works by constructing many decision trees during training and outputting the class that is the mode of the classes of the individual trees. Random forest can be used for both classification and regression problems and provides high accuracy even with large datasets.
The stage of maturity is utmost important for the harvesting of any crop. and for horticultural and plantation crops, its like the very life of those crops
Harvesting practices for special market purposePawan Nagar
1. Harvesting is the act of gathering a ripe crop from the fields using various manual and mechanical methods depending on the crop.
2. Maturity indices help determine the optimal time to harvest crops to ensure acceptable quality for consumers. Indices include factors like color, firmness, sugar and acid content, and days after flowering.
3. The presentation discusses various maturity indices for fruits like mango, banana, citrus and vegetables and the importance of harvesting at the proper maturity stage.
Lecture 1: Importance of Postharvest TechnologyKarl Obispo
The document discusses postharvest technology, including:
1. Defining postharvest technology and explaining its importance in preventing food losses, improving nutrition, adding value to agricultural products, and generating jobs.
2. The three main objectives of postharvest technology are maintaining quality, protecting food safety, and reducing losses between harvest and consumption.
3. Common causes of postharvest losses in the Philippines include rough handling, inadequate cooling and temperature control, lack of sorting, and inadequate packaging. Proper temperature management and reducing damage is key to reducing losses.
Lecture 3: Fruits and Vegetables HarvestingKarl Obispo
This document discusses harvesting of fruits and vegetables. It begins with learning objectives related to postharvest procedures, maturity indices, and harvesting practices. It then outlines topics to be covered including postharvest handling procedures, defining maturity indices, importance of maturity indices, differences between physiological and horticultural maturity, and harvesting practices for common fruits and vegetables. The document discusses factors that determine optimum maturity for harvesting, different types of maturity, maturity indices used for various fruits and vegetables, and methods for manual and mechanical harvesting. It stresses the importance of harvesting at proper maturity to ensure quality and storage life.
This document discusses fruit ripening conditions and factors that influence the ripening process. It describes how climacteric and non-climacteric fruits differ in their respiration rates during ripening. Ripening involves physical and chemical changes in the fruit brought about by ethylene exposure and environmental conditions like temperature and humidity. Key changes during ripening include softening of texture, changes in color, sugar content, acidity, and the development of flavor and aroma compounds. The role of ethylene and controlled atmosphere storage conditions in modulating the ripening process is also covered.
This document provides a technical guide book on banana cultivation in Pakistan. It begins with an acknowledgment and abstract. It then provides background information on the history of banana cultivation, major varieties grown in Pakistan, and the growth cycle of banana plants. It discusses the agronomic requirements for banana such as climate, soil, fertilizer and water needs. It outlines the major districts in Sindh province where banana is grown and production practices used there. It also covers pest and disease management, harvesting techniques, and recommendations to improve production and post-harvest management in Pakistan. The guide book aims to provide technical assistance to growers, exporters and others involved in the banana value chain in Pakistan.
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
Random forest is an ensemble machine learning algorithm that combines multiple decision trees to improve predictive accuracy. It works by constructing many decision trees during training and outputting the class that is the mode of the classes of the individual trees. Random forest can be used for both classification and regression problems and provides high accuracy even with large datasets.
This document describes research on developing an Android application to identify apple diseases using image processing techniques. An algorithm was created using OpenCV and Java that takes a picture of an apple, applies grayscale conversion for pre-processing, and uses template matching to identify if the apple has bitter rot, blister spot, or scab. The algorithm had success identifying diseases correctly in 71% of cases, though some diseases were only 33% accurate. Future work to improve accuracy could explore alternative methods like cascade classification and verifying the object is an apple first before detecting diseases. The goal is to create a cost-effective mobile tool for farmers to self-identify diseases without expert help.
This document provides an introduction to multivariate image analysis (MIA). It describes how MIA analyzes images with multiple variables at each pixel, such as hyperspectral images, and discusses tools for visualizing and extracting information from such images. TrendTool allows investigating multivariate data through simple univariate measurements, while Image Manager facilitates manipulating and analyzing image groups. Factorial techniques aid in enhancing signal-to-noise when many variables are present.
AN INTEGRATED APPROACH TO CONTENT BASED IMAGERETRIEVAL by MadhuMadhu Rock
This document summarizes an integrated approach to content-based image retrieval. It discusses extracting both color and texture features from images using color moments and local binary patterns. The system is tested on a database of 1000 images across 10 classes. Results show the integrated approach of using both color and texture features provides more accurate retrievals than using either feature alone. Evaluation metrics like precision, recall and accuracy are calculated to quantitatively analyze the system's performance. Overall, the proposed multi-feature approach is found to improve content-based image retrieval compared to single-feature methods.
An automatic fruit quality inspection system for sorting and grading of tomato fruit and defected tomato detection discussed here.The main aim of this system is to replace the manual inspection system.
This helps in speed up the process improve accuracy and efficiency and reduce time. This system collect image from camera which is placed on conveyor belt.
Then image processing is done to get required features of fruits such as texture, color and size.
Defected fruit is detected based on blob detection, color detection is done based on thresholding.
Size detection is based on binary image of tomato. Sorting is done based on color and grading is done based on size.
mangoes leaf diseases detection using SVM.pptxMadniFareed1
This document proposes using support vector machines to detect mango leaf diseases through image processing. Images of mango leaves are preprocessed, features are extracted, and the images are classified as healthy or diseased using an SVM model. K-means clustering is used to segment infected leaf areas. The goal is to develop an accurate and early detection system to help farmers and further precision agriculture practices by enabling prompt treatment of mango crops.
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
Abstract
Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.
Keywords: chemometric analysis; transgenic agricultural products and foods; near-infrared spectroscopy; hyperspectral imaging
1. Introduction
Currently, genetics is widely used in various science fields. Transgenic technology is used to transfer the genes with known functional traits, such as high yield, resistance to disease and insects, and improvement of nutritional quality, into the target organism through modern scientific and technological means, so that new varieties and products are produced by adding new functional characteristics to the recipient organism. Many countries regard transgenic technology as a strategic choice to support development. Transgenics has become a strategic focus for countries to seize the commanding heights of science and technology, and to enhance the international competitiveness of agriculture.
At present, applications of transgenic technology in various fields, including to improve crops, produce vaccines, food, etc., are experiencing a very high growth rate. As a result, genetically modified (GM) production is increasing on the global market. Genetically modified crops are cultivated in 29 countries, with an area under cultivation of 190.4 million hectares [1]. Although GM crops have advantages such as insect resistance, weed resistance, disease resistance, improved nutritional value, and increased yield [2], the use of GM technology may have unintended negative effects on food and environmental safety, and therefore, GM foods have been severely restricted in most parts of the world due to legal pressure from regulatory agencies to control the production of GM products. Thus, it is a very necessary and important task to identify GM products.
Today, several me
Customer Churn Analytics using Microsoft R OpenPoo Kuan Hoong
The document summarizes a presentation on using Microsoft R Open for customer churn analytics. It discusses using machine learning algorithms like logistic regression, support vector machines, and random forests to predict customer churn. It compares the performance of these models on a telecom customer dataset using metrics like confusion matrices and ROC curves. The presentation demonstrates building a churn prediction model in Microsoft R Open and R Tools for Visual Studio.
Saliency Based Hookworm and Infection Detection for Wireless Capsule Endoscop...IRJET Journal
This document presents a method for detecting hookworm infection and ulcers in wireless capsule endoscopy images using saliency-based segmentation. The proposed method uses multi-level superpixel segmentation followed by feature extraction of color and texture properties. A particle swarm optimization algorithm is then used to classify images as healthy or infected/ulcerous based on the extracted features. Experimental results on capsule endoscopy images demonstrate the effectiveness of the proposed method at automatically detecting abnormalities in an efficient and non-invasive manner.
Random forest is an ensemble learning technique that builds multiple decision trees and merges their predictions to improve accuracy. It works by constructing many decision trees during training, then outputting the class that is the mode of the classes of the individual trees. Random forest can handle both classification and regression problems. It performs well even with large, complex datasets and prevents overfitting. Some key advantages are that it is accurate, efficient even with large datasets, and handles missing data well.
Multi criteria decision support system on mobile phone selection with ahp and...Reza Ramezani
This document proposes using multi-criteria decision making (MCDM) approaches, specifically the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to help users select a mobile phone. It outlines the evaluation process, which involves identifying important mobile phone selection criteria, calculating criteria weights using AHP, and then using TOPSIS to rank mobile phone alternatives based on how close they are to an ideal solution and how far they are from a negative ideal solution. The document provides examples of building pairwise comparison matrices in AHP and calculating ideal and non-ideal solutions and alternative distances in TOPSIS to demonstrate the selection approach.
This article is used to give a basic information regarding the change points that occur in excel and in other files. The detection methods are proposed and they are analyzed with a real time example. The features and application of the change point is also discussed in the later. Copy the link given below and paste it in new browser window to get more information on Change Point:- http://www.transtutors.com/homework-help/statistics/change-point.aspx
GIS tools allow the handling of spatial criteria data to be assimilated and interpreted by groups of experts when evaluating solutions to complex problems.This project uses multicriteria decision analysis to support geographic targeting of interventions in crop improvement for main agricultural crops, by using an application called SIEMPRE, which is GIS aided online, and is used to elicit expert opinion to value alternative solutions utilizing the Analytical Hierarchy Process (AHP) methodology.
Wheat leaf disease detection using image processingIJLT EMAS
India is a agricultural based county where approx 70%
of population depend on agriculture. Now a days the plant
disease detection is very important because agriculture is the
backbone of the county like india. Farmer is not aware what type
of disease plant having and how to prevent them from these
diseases. To overcome from these we are going to develop a
technique in which we can able to detect plant disease using
image processing technique. This includes following steps: image
acquisition image pre-processing, feature extraction and at last
we apply a classifier know as neural network.
090528 Miller Process Forensics Talk @ Asqrwmill9716
Talk presented to local ASQ chapter. It dealt with process improvement: continuous measurement system validation and utilizing capability metrics for process forensics. Further, a program was introduced that\'s been used to optimize spare parts inventory based on a resampling approach to historical data.
This document discusses various optimization techniques used in pharmaceutical formulation and processing. It begins by defining optimization and describing how it is applied in preliminary, experimental, analytical, and verification phases. It then covers classical optimization techniques like calculus-based approaches as well as statistical experimental designs. Evolutionary operations, simplex methods, Lagrangian methods, and search methods are also summarized. Finally, applications of these optimization techniques are mentioned for drug delivery systems, pharmacokinetic studies, and culture medium optimization.
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.
This document describes research on developing an Android application to identify apple diseases using image processing techniques. An algorithm was created using OpenCV and Java that takes a picture of an apple, applies grayscale conversion for pre-processing, and uses template matching to identify if the apple has bitter rot, blister spot, or scab. The algorithm had success identifying diseases correctly in 71% of cases, though some diseases were only 33% accurate. Future work to improve accuracy could explore alternative methods like cascade classification and verifying the object is an apple first before detecting diseases. The goal is to create a cost-effective mobile tool for farmers to self-identify diseases without expert help.
This document provides an introduction to multivariate image analysis (MIA). It describes how MIA analyzes images with multiple variables at each pixel, such as hyperspectral images, and discusses tools for visualizing and extracting information from such images. TrendTool allows investigating multivariate data through simple univariate measurements, while Image Manager facilitates manipulating and analyzing image groups. Factorial techniques aid in enhancing signal-to-noise when many variables are present.
AN INTEGRATED APPROACH TO CONTENT BASED IMAGERETRIEVAL by MadhuMadhu Rock
This document summarizes an integrated approach to content-based image retrieval. It discusses extracting both color and texture features from images using color moments and local binary patterns. The system is tested on a database of 1000 images across 10 classes. Results show the integrated approach of using both color and texture features provides more accurate retrievals than using either feature alone. Evaluation metrics like precision, recall and accuracy are calculated to quantitatively analyze the system's performance. Overall, the proposed multi-feature approach is found to improve content-based image retrieval compared to single-feature methods.
An automatic fruit quality inspection system for sorting and grading of tomato fruit and defected tomato detection discussed here.The main aim of this system is to replace the manual inspection system.
This helps in speed up the process improve accuracy and efficiency and reduce time. This system collect image from camera which is placed on conveyor belt.
Then image processing is done to get required features of fruits such as texture, color and size.
Defected fruit is detected based on blob detection, color detection is done based on thresholding.
Size detection is based on binary image of tomato. Sorting is done based on color and grading is done based on size.
mangoes leaf diseases detection using SVM.pptxMadniFareed1
This document proposes using support vector machines to detect mango leaf diseases through image processing. Images of mango leaves are preprocessed, features are extracted, and the images are classified as healthy or diseased using an SVM model. K-means clustering is used to segment infected leaf areas. The goal is to develop an accurate and early detection system to help farmers and further precision agriculture practices by enabling prompt treatment of mango crops.
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
Abstract
Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.
Keywords: chemometric analysis; transgenic agricultural products and foods; near-infrared spectroscopy; hyperspectral imaging
1. Introduction
Currently, genetics is widely used in various science fields. Transgenic technology is used to transfer the genes with known functional traits, such as high yield, resistance to disease and insects, and improvement of nutritional quality, into the target organism through modern scientific and technological means, so that new varieties and products are produced by adding new functional characteristics to the recipient organism. Many countries regard transgenic technology as a strategic choice to support development. Transgenics has become a strategic focus for countries to seize the commanding heights of science and technology, and to enhance the international competitiveness of agriculture.
At present, applications of transgenic technology in various fields, including to improve crops, produce vaccines, food, etc., are experiencing a very high growth rate. As a result, genetically modified (GM) production is increasing on the global market. Genetically modified crops are cultivated in 29 countries, with an area under cultivation of 190.4 million hectares [1]. Although GM crops have advantages such as insect resistance, weed resistance, disease resistance, improved nutritional value, and increased yield [2], the use of GM technology may have unintended negative effects on food and environmental safety, and therefore, GM foods have been severely restricted in most parts of the world due to legal pressure from regulatory agencies to control the production of GM products. Thus, it is a very necessary and important task to identify GM products.
Today, several me
Customer Churn Analytics using Microsoft R OpenPoo Kuan Hoong
The document summarizes a presentation on using Microsoft R Open for customer churn analytics. It discusses using machine learning algorithms like logistic regression, support vector machines, and random forests to predict customer churn. It compares the performance of these models on a telecom customer dataset using metrics like confusion matrices and ROC curves. The presentation demonstrates building a churn prediction model in Microsoft R Open and R Tools for Visual Studio.
Saliency Based Hookworm and Infection Detection for Wireless Capsule Endoscop...IRJET Journal
This document presents a method for detecting hookworm infection and ulcers in wireless capsule endoscopy images using saliency-based segmentation. The proposed method uses multi-level superpixel segmentation followed by feature extraction of color and texture properties. A particle swarm optimization algorithm is then used to classify images as healthy or infected/ulcerous based on the extracted features. Experimental results on capsule endoscopy images demonstrate the effectiveness of the proposed method at automatically detecting abnormalities in an efficient and non-invasive manner.
Random forest is an ensemble learning technique that builds multiple decision trees and merges their predictions to improve accuracy. It works by constructing many decision trees during training, then outputting the class that is the mode of the classes of the individual trees. Random forest can handle both classification and regression problems. It performs well even with large, complex datasets and prevents overfitting. Some key advantages are that it is accurate, efficient even with large datasets, and handles missing data well.
Multi criteria decision support system on mobile phone selection with ahp and...Reza Ramezani
This document proposes using multi-criteria decision making (MCDM) approaches, specifically the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to help users select a mobile phone. It outlines the evaluation process, which involves identifying important mobile phone selection criteria, calculating criteria weights using AHP, and then using TOPSIS to rank mobile phone alternatives based on how close they are to an ideal solution and how far they are from a negative ideal solution. The document provides examples of building pairwise comparison matrices in AHP and calculating ideal and non-ideal solutions and alternative distances in TOPSIS to demonstrate the selection approach.
This article is used to give a basic information regarding the change points that occur in excel and in other files. The detection methods are proposed and they are analyzed with a real time example. The features and application of the change point is also discussed in the later. Copy the link given below and paste it in new browser window to get more information on Change Point:- http://www.transtutors.com/homework-help/statistics/change-point.aspx
GIS tools allow the handling of spatial criteria data to be assimilated and interpreted by groups of experts when evaluating solutions to complex problems.This project uses multicriteria decision analysis to support geographic targeting of interventions in crop improvement for main agricultural crops, by using an application called SIEMPRE, which is GIS aided online, and is used to elicit expert opinion to value alternative solutions utilizing the Analytical Hierarchy Process (AHP) methodology.
Wheat leaf disease detection using image processingIJLT EMAS
India is a agricultural based county where approx 70%
of population depend on agriculture. Now a days the plant
disease detection is very important because agriculture is the
backbone of the county like india. Farmer is not aware what type
of disease plant having and how to prevent them from these
diseases. To overcome from these we are going to develop a
technique in which we can able to detect plant disease using
image processing technique. This includes following steps: image
acquisition image pre-processing, feature extraction and at last
we apply a classifier know as neural network.
090528 Miller Process Forensics Talk @ Asqrwmill9716
Talk presented to local ASQ chapter. It dealt with process improvement: continuous measurement system validation and utilizing capability metrics for process forensics. Further, a program was introduced that\'s been used to optimize spare parts inventory based on a resampling approach to historical data.
This document discusses various optimization techniques used in pharmaceutical formulation and processing. It begins by defining optimization and describing how it is applied in preliminary, experimental, analytical, and verification phases. It then covers classical optimization techniques like calculus-based approaches as well as statistical experimental designs. Evolutionary operations, simplex methods, Lagrangian methods, and search methods are also summarized. Finally, applications of these optimization techniques are mentioned for drug delivery systems, pharmacokinetic studies, and culture medium optimization.
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.
Similar to Content based image retrieval for agriculture crops (20)
The importance of sustainable and efficient computational practices in artificial intelligence (AI) and deep learning has become increasingly critical. This webinar focuses on the intersection of sustainability and AI, highlighting the significance of energy-efficient deep learning, innovative randomization techniques in neural networks, the potential of reservoir computing, and the cutting-edge realm of neuromorphic computing. This webinar aims to connect theoretical knowledge with practical applications and provide insights into how these innovative approaches can lead to more robust, efficient, and environmentally conscious AI systems.
Webinar Speaker: Prof. Claudio Gallicchio, Assistant Professor, University of Pisa
Claudio Gallicchio is an Assistant Professor at the Department of Computer Science of the University of Pisa, Italy. His research involves merging concepts from Deep Learning, Dynamical Systems, and Randomized Neural Systems, and he has co-authored over 100 scientific publications on the subject. He is the founder of the IEEE CIS Task Force on Reservoir Computing, and the co-founder and chair of the IEEE Task Force on Randomization-based Neural Networks and Learning Systems. He is an associate editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
Gamify it until you make it Improving Agile Development and Operations with ...Ben Linders
So many challenges, so little time. While we’re busy developing software and keeping it operational, we also need to sharpen the saw, but how? Gamification can be a way to look at how you’re doing and find out where to improve. It’s a great way to have everyone involved and get the best out of people.
In this presentation, Ben Linders will show how playing games with the DevOps coaching cards can help to explore your current development and deployment (DevOps) practices and decide as a team what to improve or experiment with.
The games that we play are based on an engagement model. Instead of imposing change, the games enable people to pull in ideas for change and apply those in a way that best suits their collective needs.
By playing games, you can learn from each other. Teams can use games, exercises, and coaching cards to discuss values, principles, and practices, and share their experiences and learnings.
Different game formats can be used to share experiences on DevOps principles and practices and explore how they can be applied effectively. This presentation provides an overview of playing formats and will inspire you to come up with your own formats.
This presentation by Nathaniel Lane, Associate Professor in Economics at Oxford University, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
This presentation by Yong Lim, Professor of Economic Law at Seoul National University School of Law, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by Tim Capel, Director of the UK Information Commissioner’s Office Legal Service, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
This presentation by OECD, OECD Secretariat, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Why Psychological Safety Matters for Software Teams - ACE 2024 - Ben Linders.pdfBen Linders
Psychological safety in teams is important; team members must feel safe and able to communicate and collaborate effectively to deliver value. It’s also necessary to build long-lasting teams since things will happen and relationships will be strained.
But, how safe is a team? How can we determine if there are any factors that make the team unsafe or have an impact on the team’s culture?
In this mini-workshop, we’ll play games for psychological safety and team culture utilizing a deck of coaching cards, The Psychological Safety Cards. We will learn how to use gamification to gain a better understanding of what’s going on in teams. Individuals share what they have learned from working in teams, what has impacted the team’s safety and culture, and what has led to positive change.
Different game formats will be played in groups in parallel. Examples are an ice-breaker to get people talking about psychological safety, a constellation where people take positions about aspects of psychological safety in their team or organization, and collaborative card games where people work together to create an environment that fosters psychological safety.
1.) Introduction
Our Movement is not new; it is the same as it was for Freedom, Justice, and Equality since we were labeled as slaves. However, this movement at its core must entail economics.
2.) Historical Context
This is the same movement because none of the previous movements, such as boycotts, were ever completed. For some, maybe, but for the most part, it’s just a place to keep your stable until you’re ready to assimilate them into your system. The rest of the crabs are left in the world’s worst parts, begging for scraps.
3.) Economic Empowerment
Our Movement aims to show that it is indeed possible for the less fortunate to establish their economic system. Everyone else – Caucasian, Asian, Mexican, Israeli, Jews, etc. – has their systems, and they all set up and usurp money from the less fortunate. So, the less fortunate buy from every one of them, yet none of them buy from the less fortunate. Moreover, the less fortunate really don’t have anything to sell.
4.) Collaboration with Organizations
Our Movement will demonstrate how organizations such as the National Association for the Advancement of Colored People, National Urban League, Black Lives Matter, and others can assist in creating a much more indestructible Black Wall Street.
5.) Vision for the Future
Our Movement will not settle for less than those who came before us and stopped before the rights were equal. The economy, jobs, healthcare, education, housing, incarceration – everything is unfair, and what isn’t is rigged for the less fortunate to fail, as evidenced in society.
6.) Call to Action
Our movement has started and implemented everything needed for the advancement of the economic system. There are positions for only those who understand the importance of this movement, as failure to address it will continue the degradation of the people deemed less fortunate.
No, this isn’t Noah’s Ark, nor am I a Prophet. I’m just a man who wrote a couple of books, created a magnificent website: http://www.thearkproject.llc, and who truly hopes to try and initiate a truly sustainable economic system for deprived people. We may not all have the same beliefs, but if our methods are tried, tested, and proven, we can come together and help others. My website: http://www.thearkproject.llc is very informative and considerably controversial. Please check it out, and if you are afraid, leave immediately; it’s no place for cowards. The last Prophet said: “Whoever among you sees an evil action, then let him change it with his hand [by taking action]; if he cannot, then with his tongue [by speaking out]; and if he cannot, then, with his heart – and that is the weakest of faith.” [Sahih Muslim] If we all, or even some of us, did this, there would be significant change. We are able to witness it on small and grand scales, for example, from climate control to business partnerships. I encourage, invite, and challenge you all to support me by visiting my website.
This presentation by Juraj Čorba, Chair of OECD Working Party on Artificial Intelligence Governance (AIGO), was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by OECD, OECD Secretariat, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
This presentation by Katharine Kemp, Associate Professor at the Faculty of Law & Justice at UNSW Sydney, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
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Prsentation for VIVA Welike project 1semester.pptx
Content based image retrieval for agriculture crops
1. CONTENT-BASED IMAGE
RETRIEVAL FOR AGRICULTURAL
CROPS
Esraa M. Elhariri, MSc
Scientific Research Group n Egypt (SRGE)
www.egyptscience.net
2. Agenda
Introduction
Problem Definition
Scope of work & Proposed Solutions
Crops Ripeness
The Proposed Ripeness Classification System
Experimental Results
Conclusions & Future Work
3. Introduction
Product quality is one of the prime factors in
ensuring consistent marketing of crops.
Determining ripeness stages is a very
important issue in produce (fruits and
vegetables) industry for getting high quality.
Optimal harvest dates and prediction of
storage life are still mainly based on
subjective interpretation and practical
experience.
4. Introduction Cont…
Monitoring and controlling ripeness is a
very important issue in the fruit industry
since the state of ripeness during harvest,
storage and market distribution determines
the quality of the final product measured in
terms of customer satisfaction.
5. Problem Definition
Crops at greenhouse, storage containers are affected by
changes at temperature and humidity levels.
these changes can cause diseases, lost of nutritional value,
over-ripen and changes at texture, appearance and flavor and
this affect the quality of the agricultural crops.
Monitor the ripeness process of crops is very important issue
at agricultural and industry.
6. Problem Definition Cont…
So the human labors were required to visit the greenhouse and
check the humidity and temperature levels at specifics times
in according to plant type (e.g. strawberry need the
temperature level and humidity to be checked every 3 hours).
Doing this manually will be time consuming, cost high and
needs a lot of work and efforts.
7. Scope of work & Proposed
Solutions
This thesis concerns the study and development of different
approaches to help Labors via providing an automated
ripeness assessment system for agricultural crops.
Developing an automatic content based image retrieval
system for agriculture crops, which is tackling the problem
of ripeness assessment.
Having a comparative study among different classification
algorithms for the problem of ripeness classification.
13. • Resizing images to 250x250 pixels for the following
reason:
• Reducing their color index to avoid high
computation.
During this phase, the proposed approach prepares images
for the features extraction phase, The first step is :
The proposed classification
system: Pre-processing Phase
14. • Remove gray shadow pixels.
• Convert image from RGB to Gray
image.
• Subtract image from background
image and compute a binary
mask.
• Apply the binary mask to original
image.
Background Removal Algorithm
Original Images
Images after background Removal
The second step is removing
image background to get region
of interest by using the following
procedure:
The proposed classification
system: Pre-processing Phase
15. The third step is : converting each image from
RGB to HSV color space and extract HSV
components for the following reasons:
When applying classification using PCA and
Euclidean distance using both RGB and HSV color
features we got better results using HSV color.
From Survey, HSV color space as it is widely used
in the field of color vision and close to the
categories of human color perception
where R, G and B are color
component of RGB color
space
The proposed classification
system: Pre-processing Phase
16. The pre-processing phase can
be summarized as the
following:
• Resize images to 250 X 250
pixels,
• Remove images background,
• Convert images from RGB color
space to HSV color space,
•Extract HSV components.
The proposed classification
system: Pre-processing Phase
17. Since tomato surface color is the
most important characteristic to
observe ripeness of tomato, this
system uses color features for
classifying ripeness stages.
Two color descriptors which were
used here are color moments and
HSV colored histogram.
The proposed classification
system: Feature Extraction Phase
18. Standard Equation:
The first descriptor is color
moments: we used mean, standard
deviation, and skewness which have
been proved to be efficient and
effective way for representing color
distribution in any image.
Mean, standard deviation and
skewness for a colored image of
size NxM pixels are defined by the
following equations:
Mean Equation:
Skewness Equation:
The proposed classification
system: Feature Extraction Phase
19. Colored histogram is the second color descriptor that shows representation of the
distribution of colors in an image. It represents the number of pixels that have
colors in each range of colors.
We used 1D 16x4x4 HSV colored histogram, with 16 level for hue and 4 level
for each of saturation and value.
Then we compute a combination 1D Features vector of the three color moments
and HSV histogram.
The proposed classification
system: Feature Extraction Phase
20. Principal component analysis is a
statistical common technique, which is
widely used in image recognition and
compression for a dimensionality
reduction, data representation and
features extraction tool as it ensures
better classification.
we use Principal Component
Analysis(PCA) for features vector
Transformation to extract good features
for the classification purpose.
IM1 F1 F2 : : : : : : Fk
IM2 : : : : : : : : :
::: : : : : : : : : :
IMn F1 F2 Fk
IM1 F1
: : Fm
IM2 :
: : :
::: :
: : :
IMn F1
: : Fm
PCA Algorithm
Whole Features
Extracted Sub-set of Features
The proposed classification
system: Feature Extraction Phase
21. During this phase, the proposed
approach uses Support Vector
Machine(SVM) as a classifier, this
phase is responsible for classify data
into 5 classes mentioned before.
The proposed classification
system: Classification Phase
22. The Support Vector Machine (SVM) is a
Machine Learning (ML) algorithm that is
used for classification and regression of
high dimensional datasets with great
results. SVM solves the classification
problem via trying to find an optimal
separating hyperplane between classes.
SVM algorithm seeks to maximize the
margin around a hyperplane that separates
a positive class from a negative class.
The proposed classification
system: Classification Phase
23. Since SVM is a binary classifier, we have
used two different approaches to use it as
multi-class classifier:
One-against-All
One-against-One
Also we have tested our SVM classifier
using different kernel functions includes:
Linear kernel function,
Polynomial kernel function,
MLP kernel function,
RBF kernel function.
The proposed classification
system: Classification Phase
24. One against All:
1. Construct N binary SVM.
2. Each SVM separates one class
from the rest classes.
3. Train the ith SVM with all
training samples of the ith class
with positive labels, and
training samples of other classes
with negative labels.
The proposed classification
system: Classification Phase (One-against-All
Approach)
25. At the begin, we have used One-
against-All multi-class SVM with
different kernel functions and total
of 230 images used for the
classification problem, it was
divided into 175 images as a
training dataset and 55 images as
a testing dataset.
Experimental Results:
First Approach using One-against-All Approach
26. Training dataset is divided into 5 classes representing the different
stages of tomato ripeness.
Experimental Results:
Training Dataset
27. Classification accuracy
using different kernel
function and different
sizes of training
classes:
Experimental Results:
Results of First Approach
29. From the previous figure we
found that classification
accuracy using SVM with
linear kernel function
increased by increasing
number of training images per
class.
Experimental Results: Accuracy of
linear Kernel Function using Different Training Size
30. Publications List
• Implementation of one-against-all multi-calss SVM for tomato
• Publication of Multiclass SVM Based Classification Approach for
Tomato Ripeness
Submission : Jul 2013
Publication :Aug 2013
• Implementation of one-against-all & one-against-one multi-calss
SVM
• Publication of Automated Ripeness Assessment System of Tomatoes
Using PCA and SVM Techniques(book chapter)
Submission : Nov 2013
Publication: June 2014
• Implementation of one-against-one multi-calss SVM for bell pepper
• Publication of Bell Pepper Ripeness Classification based on Support Vector
Machine
Submission : Dec 2013
Presentation: Apr 2014
• Implementation of different machine learning techniques for tomato as an
extension of the first paper and the book chapter.
• Publication of Using machine learning techniques for evaluating tomato
ripeness
Submission : Feb 2014
Publication: Oct 2014
• Comparative Study of different classification techniques for both crops.
• Publication of Random Forests Based Classification for Crops Ripeness Stages
Submission: Apr 2014
Publication : June 2014
Esraa Elhariri, Nashwa El-Bendary, Mohamed Mostafa M. Fouad, Jan Plato’s,
Aboul Ella Hassanien, Ahmed M. M. Hussein, “Multiclass SVM Based
Classification Approach for Tomato Ripeness.” In Proceedings of the Fourth
International Conference on Innovations in Bio-inspired Computing and
Applications(IBICA 2013), 22 – 24 August, Ostrava, Czech Republic, pp.175-186 ,
2013, Springer International Publishing.
31. Then, we have used both of
One-against-All & One-against-
One with 10-fold cross-
validation to generalize model
accuracy, using total of 250
images for both of training and
testing.
This figure shows how cross-
validation works.
TrainingTesting
Experimental Results: Second
Approach using both of OAO and OAA with Cross-
Validation
32. One-against-One:
1-train k(k − 1)/2 binary SVMs
(1, 2), (1, 3), . . . , (1, k), (2, 3), (2,
4), . . . , (k − 1, k),
2-Select the one with the largest
vote.
The proposed classification
system: Classification Phase (One-against-One
Approach)
33. Classification accuracy using different kernel function and total number of
250 image(used for both training and testing using 10 folds cross-
validation) using one against one approach for multi-class SVM
classification:
Experimental Results: One-against-
One(OAO) with 10 fold Cross-Validation Approach
34. Classification accuracy using different kernel function and total number of
250 image(used for both training and testing using 10 folds cross-
validation) using one against all approach for multi-class SVM
classification:
Experimental Results: One-against-
All(OAA) with 10 fold Cross-Validation Approach
35. The following figure show a comparison between the classification
accuracy of One-against-All(OAA) multi-class SVM and One-against-
One(OAO) multi-class SVM Approaches:
Experimental Results: Comparison
between of OAO & OAA Approaches
36. Publications List
• Implementation of one-against-all multi-calss SVM for tomato
• Publication of Multiclass SVM Based Classification Approach for
Tomato Ripeness
Submission : Jul 2013
Publication :Aug 2013
• Implementation of one-against-all & one-against-one multi-calss
SVM
• Publication of Automated Ripeness Assessment System of Tomatoes
Using PCA and SVM Techniques(book chapter)
Submission : Nov 2013
Publication: June 2014
• Implementation of one-against-one multi-calss SVM for bell pepper
• Publication of Bell Pepper Ripeness Classification based on Support Vector
Machine
Submission : Dec 2013
Presentation: Apr 2014
• Implementation of different machine learning techniques for tomato as an
extension of the first paper and the book chapter.
• Publication of Using machine learning techniques for evaluating tomato
ripeness
Submission : Feb 2014
Publication: Oct 2014
• Comparative Study of different classification techniques for both crops.
• Publication of Random Forests Based Classification for Crops Ripeness Stages
Submission: Apr 2014
Publication : June 2014
Esraa Elhariri, Nashwa El-Bendary, Aboul Ella Hassanien, Amr Badr:
"Automated Ripeness Assessment System of Tomatoes Using PCA and SVM
Techniques." Computer Vision and Image Processing in Intelligent Systems and
Multimedia Technologies. IGI Global, 2014. 101-130. Web. 9 Oct. 2014.
doi:10.4018/978-1-4666-6030-4.ch006.
38. we have used One-against-One
multi-class SVM with different
kernel functions and total of 175
images used for the classification
problem, they were used for both
training and testing dataset using
10 fold Cross-validation.
Experimental Results:
One-against-One Approach
39. Training dataset is divided into 5 classes
representing the different stages of Bell Pepper
ripeness.
Experimental Results:
Training Dataset
40. Experimental Results: One-against-
One (OAO) with 10 Cross-validation Approach
Classification accuracy using
different kernel function and
10-folds cross-validation.
41.
42.
43. Publications List
• Implementation of one-against-all multi-calss SVM for tomato
• Publication of Multiclass SVM Based Classification Approach for
Tomato Ripeness
Submission : Jul 2013
Publication :Aug 2013
• Implementation of one-against-all & one-against-one multi-calss
SVM
• Publication of Automated Ripeness Assessment System of Tomatoes
Using PCA and SVM Techniques(book chapter)
Submission : Nov 2013
Publication: June 2014
• Implementation of one-against-one multi-calss SVM for bell pepper
• Publication of Bell Pepper Ripeness Classification based on Support Vector
Machine
Submission : Dec 2013
Presentation: Apr 2014
• Implementation of different machine learning techniques for tomato as an
extension of the first paper and the book chapter.
• Publication of Using machine learning techniques for evaluating tomato
ripeness
Submission : Feb 2014
Publication: Oct 2014
• Comparative Study of different classification techniques for both crops.
• Publication of Random Forests Based Classification for Crops Ripeness Stages
Submission: Apr 2014
Publication : June 2014
Esraa Elhariri, Nashwa El-Bendary, Ahmed M. M. Hussein, Aboul Ella
Hassanien, and Amr Badr, “Bell Pepper Ripeness Classification based on Support
Vector Machine”, In Proceeding of IEEE ICET, 19 – 21 April, Cairo, Egypt.
45. Then, we have used both of
One-against-All & One-against-
One, In addition to, LDA with
10-fold cross-validation to
generalize model accuracy,
using total of 250 images for
both of training and testing.
This figure shows how cross-
validation works.
TrainingTesting
Experimental Results: Second Approach
using both of OAO, OAA and LDA with Cross-Validation
46. Linear Discriminant Analysis
(LDA)
LDA is a commonly used
technique for data classification
and dimensionality reduction.
It’s basic idea is to find a linear
transformation that best
discriminate among classes, then
classification can be performed in
transformed space based on some
metrics(Euclidean distance)
47. Classification accuracy using different kernel function and total number of 250
image(used for both training and testing using 10 folds cross-validation) using one
against one approach for multi-class SVM classification:
Experimental Results: One-against-
One (OAO) with 10 Cross-validation Approach
48.
49.
50. Classification accuracy using different kernel function and total number of 250
image(used for both training and testing using 10 folds cross-validation) using one
against all approach for multi-class SVM classification:
Experimental Results: One-against-All
(OAA) with 10 Cross-validation Approach
51.
52.
53. Classification accuracy using total number of 250 image(used for both training
and testing using 10 folds cross-validation) using LDA classification:
Accuracy= 84 %
Experimental Results LDA with 10
Cross-validation Approach
54. The following figure show a comparison between the
classification accuracy of One-against-All(OAA) multi-class
SVM and One-against-One(OAO) multi-class SVM and LDA
Approaches:
Experimental Results: Comparison
between of OAO & OAA and LDA Approaches
55. Publications List
• Implementation of one-against-all multi-calss SVM for tomato
• Publication of Multiclass SVM Based Classification Approach for
Tomato Ripeness
Submission : Jul 2013
Publication :Aug 2013
• Implementation of one-against-all & one-against-one multi-calss
SVM
• Publication of Automated Ripeness Assessment System of Tomatoes
Using PCA and SVM Techniques(book chapter)
Submission : Nov 2013
Publication: June 2014
• Implementation of one-against-one multi-calss SVM for bell pepper
• Publication of Bell Pepper Ripeness Classification based on Support
Vector Machine
Submission : Dec 2013
Presentation: Apr 2014
• Implementation of different machine learning techniques for
tomato as an extension of the first paper and the book chapter.
• Publication of Using machine learning techniques for evaluating
tomato ripeness
Submission : Feb 2014
Publication: Oct 2014
• Comparative Study of different classification techniques for both crops.
• Publication of Random Forests Based Classification for Crops Ripeness
Stages
Submission: Apr 2014
Publication : June 2014
Nashwa El-Bendary, Esraa El Hariri, Aboul Ella Hassanien, Amr Badr, “Using
machine learning techniques for evaluating tomato ripeness”, In International
Journal of Expert Systems with Applications, Volume 42, Issue 4, March 2015,
Pages 1892-1905, Elsevier, ISSN 0957-4174,
http://dx.doi.org/10.1016/j.eswa.2014.09.057.
57. Experimental Results:
One-against-One Approach
we have used One-against-One multi-
class SVM and Random Forests with
different kernel functions and total of 175
and 250 images for bell pepper and
tomato, respectively used for the
classification problem, they were used
for both training and testing dataset.
58. The proposed classification
system: Classification Phase
The Random Forests (RF) is one of the best
known classification and regression
techniques, which has the ability to classify
large dataset with excellent accuracy.
Random Forests algorithm generates an
ensemble of decision trees. Ensemble
methods main principle is to group weak
learners together to build a strong learner.
59. The proposed classification
system: Classification Phase
The input is entered at the top and as it traverses
down the tree, the original data is sampled in
random, but with replacement into smaller and
smaller sets.
The class of sample is determined using random
forests trees, which are of an arbitrary number.
62. Experimental Results: One-against-One
(OAO) & RF with 10 Cross-validation Approaches
Bell Pepper Ripeness
Classification accuracy using
Random Forest and SVM
using different kernel function
and 10-folds cross-validation.
63. Experimental Results: One-against-One
(OAO) & RF with 10 Cross-validation Approaches
Tomato Ripeness Classification
accuracy using Random Forest
and SVM using different kernel
function and 10-folds cross-
validation.
64. Publications List
• Implementation of one-against-all multi-calss SVM for tomato
• Publication of Multiclass SVM Based Classification Approach for
Tomato Ripeness
Submission : Jul 2013
Publication :Aug 2013
• Implementation of one-against-all & one-against-one multi-calss
SVM
• Publication of Automated Ripeness Assessment System of Tomatoes
Using PCA and SVM Techniques(book chapter)
Submission : Nov 2013
Publication: June 2014
• Implementation of one-against-one multi-calss SVM for bell pepper
• Publication of Bell Pepper Ripeness Classification based on Support Vector
Machine
Submission : Dec 2013
Presentation: Apr 2014
• Implementation of different machine learning techniques for tomato as an
extension of the first paper and the book chapter.
• Publication of Using machine learning techniques for evaluating tomato
ripeness
Submission : Feb 2014
Publication: Oct 2014
• Comparative Study of different classification techniques for both crops.
• Publication of Random Forests Based Classification for Crops Ripeness Stages
Submission: Apr 2014
Publication : June 2014
Esraa Elhariri, Nashwa El-Bendary, Aboul Ella Hassanien, Amr Badr, Ahmed M.
M. Hussein, Václav Snásel, “Random Forests Based Classification for Crops
Ripeness Stages”, In Proceedings of the Fifth International Conference on
Innovations in Bio-Inspired Computing and Applications (IBICA 2014), vol. 303,
23 – 25 June, Ostrava, Czech Republic, pp.205-215, 2014, Springer International
Publishing.
65. Conclusions & Future Work
In this Thesis, a system for classifying the ripeness stages of
both tomato and bell pepper has been developed.
For tomato ripeness assessment, the ripeness classification
accuracy obtained by the OAO multi-class SVM approach is
better than ripeness classification accuracy obtained by the
OAA multi-class SVM and LDA approaches.
For bell pepper ripeness assessment, the ripeness classification
accuracy obtained by the OAO multi-class SVM approach is
better than ripeness classification accuracy obtained by the
OAA multi-class SVM approach.
66. Conclusions & Future Work
A number of future research could be achieved via classifying
different objects or crops by involving other features (texture,
shape, size, ... etc.) according to the classified objects nature.
Other Machine Learning approaches could be employed in
order to address the advantages and limitations of applying
each of them.
Another direction of research is to use nondestructive/non-
invasive detection technologies of food quality/maturity such
as hyperspectral imaging systems, colorimetric, Near Infrared
Spectroscopy, and non-invasive smart sensing technologies