Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widel...Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural production.This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational complexity.The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy.Using transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant diseases.To evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,respectively.In identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.展开更多
基金supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and granted financial resources from the Ministry of Trade,Industry,and Energy,Republic of Korea(No.20204010600090)The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups.Project under grant number(R.G.P.1/257/43).
文摘Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural production.This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational complexity.The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy.Using transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant diseases.To evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,respectively.In identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.
