Corn Disease Detection Based on an Improved YOLOX-Tiny Network Model

Shanni Li, Zhensheng Yang, Huabei Nie and Xiao Chen
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Shanni Li: Digital Grid Research Institute, China
Zhensheng Yang: South China Agricultural University, China
Huabei Nie: Dongguan City University, China
Xiao Chen: Shenzhen Institute of Information Technology, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2022, vol. 16, issue 1, 1-8

Abstract: In order to detect corn diseases accurately and quickly and reduce the impact of corn diseases on yield and quality, this paper proposes an improved object detection network named YOLOX-Tiny, which fuses convolutional attention module (CBAM), mixup data enhancement strategy, and center IOU loss function. The detection network uses the CSPNet network model as the backbone network and adds the CBAM to the feature pyramid network (FPN) of the structure, which re-assigns the feature maps' weight of different channels to enhance the extraction of deep information from the structure. The performance evaluation and comparison results of the methods show that the improved YOLOX-Tiny object detection network can effectively detect three common corn diseases, such as cercospora grayspot, northern blight, and commonrust. Compared with the traditional neural network models (90.89% of VGG-16, 97.32% of YOLOv4-tiny, 97.85% of YOLOX-Tiny, 97.91% of ResNet-50, and 97.31% of Faster RCNN), the presented improved YOLOX-Tiny network has higher accuracy.

Date: 2022
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Citations: View citations in EconPapers (1)

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