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

Author:

Li Shanni1,Yang Zhensheng2,Nie Huabei3,Chen Xiao4

Affiliation:

1. Digital Grid Research Institute, China

2. South China Agricultural University, China

3. Dongguan City University, China

4. 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.

Publisher

IGI Global

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Reference9 articles.

1. Ge, Z., Liu, S., Wang, F., & Li, Z. S. J. (2021). YOLOX: Exceeding YOLO Series in2021. arXiv preprint arXiv:2107.08430.

2. Research on maize disease recognition based on image processing.;R.Gong;Modern Electronic Technology,2021

3. Spatial pyramid pooling in deep convolutional networks for visual recognition.;K.He;IEEE Transactions on Pattern Analysis and Machine Intelligence,2015

4. Corn disease image recognition based on attention mechanism asymmetric residual network and transfer learning.;Q.Li;Kexue Jishu Yu Gongcheng

5. Liuaoyu, Wuyunzhi, & Zhuxiaoning. (2021). Corn disease identification based on depth residual network. Jiangsu Agricultural Journal, 37(1), 67-74.

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