为明确云南咖啡锈菌小种类型,采用国际通用的19个咖啡锈菌生理小种鉴定寄主,利用人工接种鉴定的方法,对采自云南咖啡主产区12县(市)的51份咖啡锈菌进行鉴定,鉴定出9个小种,分别为VIII(v2,3,5)、XXXIII(v5,7 or v5,7,9)、XXXIV(v2,5,7 or...为明确云南咖啡锈菌小种类型,采用国际通用的19个咖啡锈菌生理小种鉴定寄主,利用人工接种鉴定的方法,对采自云南咖啡主产区12县(市)的51份咖啡锈菌进行鉴定,鉴定出9个小种,分别为VIII(v2,3,5)、XXXIII(v5,7 or v5,7,9)、XXXIV(v2,5,7 or v2,5,7,9)、XXXVII(v2,5,6,7,9)、XLI(v2,5,8)、XLII(v2,5,7,8 or v2,5,7,8,9)、New race(v2,5,6,7)、New race(v1,2,5,7 or v1,2,5,7,9)、New race(v1,5,7 or v1,5,7,9),这9个小种均为国内首次鉴定,其中小种XXXIII、XXXIV、New race(v2,5,6,7)、New race(v1,5,7 or v1,5,7,9)、New race(v1,2,5,7 or v1,2,5,7,9)、XLI和XLII均侵染Catimor7963,小种XXXVII侵染CatimorT5175。展开更多
Coffee is an important agricultural commodity,and its production is threatened by various diseases.It is also a source of concern for coffee-exporting countries,which is causing them to rethink their strategies for th...Coffee is an important agricultural commodity,and its production is threatened by various diseases.It is also a source of concern for coffee-exporting countries,which is causing them to rethink their strategies for the future.Maintaining crop production requires early diagnosis.Notably,Coffee Leaf Miner(CLM)Machine learning(ML)offers promising tools for automated disease detection.Early detection of CLM is crucial for minimising yield losses.However,this study explores the effectiveness of using Convolutional Neural Networks(CNNs)with transfer learning algorithms ResNet50,DenseNet121,MobileNet,Inception,and hybrid VGG19 for classifying coffee leaf images as healthy or CLM-infected.Leveraging the JMuBEN1 dataset,the proposed hybrid VGG19 model achieved exceptional performance,reaching 97%accuracy on both training and validation data.Additionally,high scores for precision,recall,and F1-score.The confusion matrix shows that all the test samples were correctly classified,which indicates the model’s strong performance on this dataset,demonstrating that the model is effective in distinguishing between healthy and CLM-infected leaves.This suggests strong potential for implementing this approach in real-world coffee plantations for early disease detection and improved disease management,and adapting it for practical deployment in agricultural settings.As well as supporting farmers in detecting diseases using modern,inexpensive methods that do not require specialists,and utilising deep learning technologies.展开更多
文摘为明确云南咖啡锈菌小种类型,采用国际通用的19个咖啡锈菌生理小种鉴定寄主,利用人工接种鉴定的方法,对采自云南咖啡主产区12县(市)的51份咖啡锈菌进行鉴定,鉴定出9个小种,分别为VIII(v2,3,5)、XXXIII(v5,7 or v5,7,9)、XXXIV(v2,5,7 or v2,5,7,9)、XXXVII(v2,5,6,7,9)、XLI(v2,5,8)、XLII(v2,5,7,8 or v2,5,7,8,9)、New race(v2,5,6,7)、New race(v1,2,5,7 or v1,2,5,7,9)、New race(v1,5,7 or v1,5,7,9),这9个小种均为国内首次鉴定,其中小种XXXIII、XXXIV、New race(v2,5,6,7)、New race(v1,5,7 or v1,5,7,9)、New race(v1,2,5,7 or v1,2,5,7,9)、XLI和XLII均侵染Catimor7963,小种XXXVII侵染CatimorT5175。
文摘Coffee is an important agricultural commodity,and its production is threatened by various diseases.It is also a source of concern for coffee-exporting countries,which is causing them to rethink their strategies for the future.Maintaining crop production requires early diagnosis.Notably,Coffee Leaf Miner(CLM)Machine learning(ML)offers promising tools for automated disease detection.Early detection of CLM is crucial for minimising yield losses.However,this study explores the effectiveness of using Convolutional Neural Networks(CNNs)with transfer learning algorithms ResNet50,DenseNet121,MobileNet,Inception,and hybrid VGG19 for classifying coffee leaf images as healthy or CLM-infected.Leveraging the JMuBEN1 dataset,the proposed hybrid VGG19 model achieved exceptional performance,reaching 97%accuracy on both training and validation data.Additionally,high scores for precision,recall,and F1-score.The confusion matrix shows that all the test samples were correctly classified,which indicates the model’s strong performance on this dataset,demonstrating that the model is effective in distinguishing between healthy and CLM-infected leaves.This suggests strong potential for implementing this approach in real-world coffee plantations for early disease detection and improved disease management,and adapting it for practical deployment in agricultural settings.As well as supporting farmers in detecting diseases using modern,inexpensive methods that do not require specialists,and utilising deep learning technologies.
