One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions.These diseases can decimate crops,disrupt food supply chains...One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions.These diseases can decimate crops,disrupt food supply chains,and escalate the risk of food shortages,underscoring the urgency of implementing robust strategies to safeguard the world’s food sources.Deep learning methods have revolutionized the field of plant disease detection,offering advanced and accurate solutions for early identification and management.However,a recurring problem in deep learning models is their susceptibility to a lack of robustness and generalization when facing novel crop and disease types that were not included in the training dataset.In this paper,we address this issue by proposing a novel deep learning-based system capable of recognizing diseased and healthy leaves across different crops,even if the system was not trained on them.The key idea is to focus on recognizing the diseased small leaf regions rather than the overall appearance of the diseased leaf,along with determining the disease’s prevalence rate on the entire leaf.For efficient classification and to leverage the excellence of the Inception model in disease recognition,we employ a small Inception model architecture,which is suitable for processing small regions without compromising performance.To confirm the effectiveness of our method,we trained and tested it using the widely acclaimed PlantVillage dataset,recognized as the most utilized dataset for its comprehensive and diverse coverage.Our method achieved an accuracy rate of 94.04%.Furthermore,when tested on new datasets,it achieved an accuracy rate of 97.13%.This innovative approach not only enhances the accuracy of plant disease detection but also addresses the critical challenge of model generalization to diverse crops and diseases.In addition,it outperformed the existing methods in its ability to identify any disease across any crop type,showcasing its potential for broad applicability and contribution to global food security initiatives.展开更多
文摘One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions.These diseases can decimate crops,disrupt food supply chains,and escalate the risk of food shortages,underscoring the urgency of implementing robust strategies to safeguard the world’s food sources.Deep learning methods have revolutionized the field of plant disease detection,offering advanced and accurate solutions for early identification and management.However,a recurring problem in deep learning models is their susceptibility to a lack of robustness and generalization when facing novel crop and disease types that were not included in the training dataset.In this paper,we address this issue by proposing a novel deep learning-based system capable of recognizing diseased and healthy leaves across different crops,even if the system was not trained on them.The key idea is to focus on recognizing the diseased small leaf regions rather than the overall appearance of the diseased leaf,along with determining the disease’s prevalence rate on the entire leaf.For efficient classification and to leverage the excellence of the Inception model in disease recognition,we employ a small Inception model architecture,which is suitable for processing small regions without compromising performance.To confirm the effectiveness of our method,we trained and tested it using the widely acclaimed PlantVillage dataset,recognized as the most utilized dataset for its comprehensive and diverse coverage.Our method achieved an accuracy rate of 94.04%.Furthermore,when tested on new datasets,it achieved an accuracy rate of 97.13%.This innovative approach not only enhances the accuracy of plant disease detection but also addresses the critical challenge of model generalization to diverse crops and diseases.In addition,it outperformed the existing methods in its ability to identify any disease across any crop type,showcasing its potential for broad applicability and contribution to global food security initiatives.
