Wheat leaf disease identification based on deep learning algorithms

Abstract

Wheat is one of the most important crops and food sources in the world. However, wheat leaf diseases have a major impact on growth. An accurate diagnosis of wheat leaf diseases is essential for wheat quality and the agricultural economy. To improve the identification precision of wheat leaf diseases, we propose an integrated deep learning algorithm, which combines a residual channel attention block (RCAB), a feedback block (FB), elliptic metric learning (EML), and a convolutional neural network (CNN) and call it RFE-CNN. First, we utilized two parallel CNNs to extract the basic features of healthy and diseased wheat leaves, respectively. Second, we used residual channel attention blocks to optimize the basic features. Third, we used feedback blocks to train the previous features. Finally, we sent these features into a CNN and elliptic metric learning for processing and classification. The experimental results demonstrate that the proposed model is superior to VGG-19, ZFNet, GoogLeNet, Inception-V4, and Efficient-B7 in some aspects, such as shorter time consumption, higher recognition precision, and stronger adaptive ability. The overall classification accuracy was 98.83%, and the maximum testing accuracy was 99.95%. We obtained an average accuracy score of 99.50% on the open-source databases viz., CGIAR, Plant Diseases, LWDCD 2020, and Plant Pathology. The proposed method has a good reference for the promotion of intelligent crop disease and insect pest detection. The recognition rate is relatively low for the samples of different ecological locations and wheat varieties. Therefore, our algorithms need to be further improved to achieve a better balance. We will use hyperspectral imaging technology to obtain more spectral data on wheat leaf diseases and send them into deep learning models for classification research.