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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Shanni Li & Zhensheng Yang & Huabei Nie & Xiao Chen, 2022. "Corn Disease Detection Based on an Improved YOLOX-Tiny Network Model," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 16(1), pages 1-8, January.
    • Handle: RePEc:igg:jcini0:v:16:y:2022:i:1:p:1-8
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    Cited by:

    1. Ssu-Han Chen & Jer-Huan Jang & Meng-Jey Youh & Yen-Ting Chou & Chih-Hsiang Kang & Chang-Yen Wu & Chih-Ming Chen & Jiun-Shiung Lin & Jin-Kwan Lin & Kevin Fong-Rey Liu, 2023. "Real-Time Video Smoke Detection Based on Deep Domain Adaptation for Injection Molding Machines," Mathematics, MDPI, vol. 11(17), pages 1-18, August.

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