Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species.Consequently,the identification of leaf diseases is essential in this field of agriculture.Dis...Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species.Consequently,the identification of leaf diseases is essential in this field of agriculture.Diseases brought on by bacteria,viruses,and fungi are a significant factor in reduced crop yields.Numerous machine learning models have been applied in the identification of plant diseases,however,with the recent developments in deep learning,this field of study seems to hold huge potential for improved accuracy.This study presents an effective method that uses image processing and deep learning approaches to distinguish between healthy and infected leaves.To effectively identify leaf diseases,we employed pre-trained models based on Convolutional Neural Networks(CNNs).There are four deepneural networks approaches used in this study:ConvolutionalNeuralNetwork(CNN),Inception-V3,Dense Net-121,and VGG-16.Our focus was on optimizing the hyper-parameters of these deep learningmodels with prior training.For the evaluation of these deep neural networks,standard evaluation measures are used,such as F1-score,recall,precision,accuracy,and AreaUnderCurve(AUC).The overall outcomes showthe better performance of Inception-V3 with an achieved accuracy of 95.5%,as well as the performance of DenseNet-121 with an accuracy of 94.4%.VGG-16 performed well as well,with an accuracy of 93.3%,and CNN achieved an accuracy of 91.9%.展开更多
Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way.Traditionally,human experts have been relied upon to diagnose anomalies in plants caused by diseases,p...Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way.Traditionally,human experts have been relied upon to diagnose anomalies in plants caused by diseases,pests,nutritional deficiencies or extreme weather.However,this is expensive,time consuming and in some cases impractical.To counter these challenges,research into the use of image processing techniques for plant disease recognition has become a hot research topic.In this paper,we provide a comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques.We hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing techniques.In particular,we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB cameras.We report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using handcrafted features.Researchers have reported high recognition accuracies on particular datasets but in many cases,the performance of those systems deteriorated significantly when tested on different datasets or in field conditions.Nevertheless,progress made so far has been encouraging.Experimental results showing the leaf disease recognition performance of ten CNN architectures in terms of recognition accuracy,recall,precision,specificity,F1-score,training duration and storage requirements are also presented.Subsequently,recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing environments.We also discuss some of the unresolved challenges that need to be addressed in order to develop practical automatic plant disease recognition systems for use in field conditions.展开更多
文摘Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species.Consequently,the identification of leaf diseases is essential in this field of agriculture.Diseases brought on by bacteria,viruses,and fungi are a significant factor in reduced crop yields.Numerous machine learning models have been applied in the identification of plant diseases,however,with the recent developments in deep learning,this field of study seems to hold huge potential for improved accuracy.This study presents an effective method that uses image processing and deep learning approaches to distinguish between healthy and infected leaves.To effectively identify leaf diseases,we employed pre-trained models based on Convolutional Neural Networks(CNNs).There are four deepneural networks approaches used in this study:ConvolutionalNeuralNetwork(CNN),Inception-V3,Dense Net-121,and VGG-16.Our focus was on optimizing the hyper-parameters of these deep learningmodels with prior training.For the evaluation of these deep neural networks,standard evaluation measures are used,such as F1-score,recall,precision,accuracy,and AreaUnderCurve(AUC).The overall outcomes showthe better performance of Inception-V3 with an achieved accuracy of 95.5%,as well as the performance of DenseNet-121 with an accuracy of 94.4%.VGG-16 performed well as well,with an accuracy of 93.3%,and CNN achieved an accuracy of 91.9%.
文摘Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way.Traditionally,human experts have been relied upon to diagnose anomalies in plants caused by diseases,pests,nutritional deficiencies or extreme weather.However,this is expensive,time consuming and in some cases impractical.To counter these challenges,research into the use of image processing techniques for plant disease recognition has become a hot research topic.In this paper,we provide a comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques.We hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing techniques.In particular,we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB cameras.We report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using handcrafted features.Researchers have reported high recognition accuracies on particular datasets but in many cases,the performance of those systems deteriorated significantly when tested on different datasets or in field conditions.Nevertheless,progress made so far has been encouraging.Experimental results showing the leaf disease recognition performance of ten CNN architectures in terms of recognition accuracy,recall,precision,specificity,F1-score,training duration and storage requirements are also presented.Subsequently,recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing environments.We also discuss some of the unresolved challenges that need to be addressed in order to develop practical automatic plant disease recognition systems for use in field conditions.
