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Recent advances in image processing techniques for automated leaf pest and disease recognition – A review

Information Processing in Agriculture 2021年第1期8卷 27-51页

作者： Lawrence C.Ngugi moataz abelwahab Mohammed Abo-Zahhad Department of Electronics and Communication Engineering Egypt-Japan University of Science and TechnologyP.O.Box 179New Borg El-Arab CityPostal Code 21934AlexandriaEgypt Department of Electrical and Electronics Engineering Assiut UniversityP.O.Box 71515AssiutEgypt

Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable ***,human experts have been relied upon to diagnose anomalies in plants caused by diseases,pests,nutritional deficiencies or extreme ***,this is expensive,time consuming and in some cases *** counter these challenges,research into the use of image processing techniques for plant disease recognition has become a hot research *** 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 *** hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing *** particular,we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB *** report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using handcrafted *** 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 ***,progress made so far has been *** 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 ***,recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing *** 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.

关键词： Precision agriculture Machine learning Plant disease recognition Image processing Convolutional neural networks

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