Abstract:

: Crop disease seriously affects food security and causes huge economic losses. In recent years, the technology of computer vision based on convolutional neural networks (CNNs) has been widely used to classify crop disease. However, the classification of fine-grained crop disease is still a challenging task due to the difficult identification of representative disease characteristics. We consider that the key to fine-grained crop disease identification lies in expanding the effective receptive field of the network and filtering key features. In this paper, a novel module (DC-DPCA) for fine-grained crop disease classification was proposed. DC-DPCA consists of two main components: (1) dilated convolution block, and (2) dual-pooling channel attention module. Specifically, the dilated convolution block is designed to expand the effective receptive field of the network, allowing the network to acquire information from a larger range of images, and to provide effective information input to the dual-pooling channel attention module. The dual-pooling channel attention module can filter out discriminative features more effectively by combining two pooling operations and constructing correlations between global and local information. The experimental results show that compared with the original networks (85.38%, 83.22%, 83.85%, 84.60%), ResNet50, VGG16, MobileNetV2, and InceptionV3 embedded with the DC-DPCA module obtained higher accuracy (87.14%, 86.26%, 86.24%, and 86.77%). We also provide three visualization methods to fully validate the rationality and effectiveness of the proposed method in this paper. These findings are crucial by effectively improving classification ability of fine-grained crop disease by CNNs. Moreover, the DC-DPCA module can be easily embedded into a variety of network structures with minimal time cost and memory cost, which contributes to the realization of smart agriculture.

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