Crop Disease Detection against Complex Background Based on Improved Atrous Spatial Pyramid Pooling

Abstract

Timely crop disease detection, pathogen identification, and infestation severity assessments can aid disease prevention and control efforts to mitigate crop-yield decline. However, improved disease monitoring methods are needed that can extract high-resolution, accurate, and rich color and spatial features from leaf disease spots in the field to achieve precise fine-grained disease-severity classification and sensitive disease-recognition accuracy. Here, we propose a neural-network-based method incorporating an improved Rouse spatial pyramid pooling strategy to achieve crop disease detection against a complex background. For neural network construction, first, a dual-attention module was introduced into the cross-stage partial network backbone to enable extraction of multi-dimensional disease information from the channel and space perspectives. Next, a dilated convolution-based spatial pyramid pooling module was integrated within the network to broaden the scope of the collection of crop-disease-related information from images of crops in the field. The neural network was tested using a set of sample data constructed from images collected at a rate of 40 frames per second that occupied only 17.12 MB of storage space. Field data analysis conducted using the miniaturized model revealed an average precision rate approaching 90.15% that exceeded the corresponding rates obtained using comparable conventional methods. Collectively, these results indicate that the proposed neural network model simplified disease-recognition tasks and suppressed noise transmission to achieve a greater accuracy rate than is obtainable using similar conventional methods, thus demonstrating that the proposed method should be suitable for use in practical applications related to crop disease recognition.