Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or compu...Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.展开更多
采用微型蒸渗仪(M icro-lys im eter)观测了单作和间作两种不同种植模式下冬小麦棵间土壤蒸发,分析了两种不同种植模式下棵间土壤蒸发的变化规律,探讨了影响麦田棵间土壤蒸发的主要因素,在此基础上对单作和间作麦田采用多元回归分析,建...采用微型蒸渗仪(M icro-lys im eter)观测了单作和间作两种不同种植模式下冬小麦棵间土壤蒸发,分析了两种不同种植模式下棵间土壤蒸发的变化规律,探讨了影响麦田棵间土壤蒸发的主要因素,在此基础上对单作和间作麦田采用多元回归分析,建立了两种种植模式下估算棵间土壤蒸发的数学模型,所建模型模拟精度较高,模拟值与实测值吻合较好。展开更多
文摘Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.
文摘采用微型蒸渗仪(M icro-lys im eter)观测了单作和间作两种不同种植模式下冬小麦棵间土壤蒸发,分析了两种不同种植模式下棵间土壤蒸发的变化规律,探讨了影响麦田棵间土壤蒸发的主要因素,在此基础上对单作和间作麦田采用多元回归分析,建立了两种种植模式下估算棵间土壤蒸发的数学模型,所建模型模拟精度较高,模拟值与实测值吻合较好。
