Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level

Xin Xu; Jing Shi; Yongqin Chen; Qiang He; Liangliang Liu; Tong Sun; Ruifeng Ding; Yanhui Lu; Chaoqun Xue; Hongbo Qiao

doi:10.3389/fpls.2023.1200901

Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level

Front Plant Sci. 2023 Aug 14:14:1200901. doi: 10.3389/fpls.2023.1200901. eCollection 2023.

Authors

Xin Xu¹, Jing Shi¹, Yongqin Chen¹, Qiang He¹, Liangliang Liu¹, Tong Sun¹, Ruifeng Ding², Yanhui Lu³, Chaoqun Xue⁴, Hongbo Qiao¹

Affiliations

¹ College of Information and Management Science, Henan Agricultural University, Zhengzhou, China.
² Institute of Plant Protection, Xinjiang Academy of Agricultural Sciences, Urumqi, China.
³ Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.
⁴ Zhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, China.

Abstract

Aphis gossypii Glover is a major insect pest in cotton production, which can cause yield reduction in severe cases. In this paper, we proposed the A. gossypii infestation monitoring method, which identifies the infestation level of A. gossypii at the cotton seedling stage, and can improve the efficiency of early warning and forecasting of A. gossypii, and achieve precise prevention and cure according to the predicted infestation level. We used smartphones to collect A. gossypii infestation images and compiled an infestation image data set. And then constructed, trained, and tested three different A. gossypii infestation recognition models based on Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once (YOLO)v5 and single-shot detector (SSD) models. The results showed that the YOLOv5 model had the highest mean average precision (mAP) value (95.7%) and frames per second (FPS) value (61.73) for the same conditions. In studying the influence of different image resolutions on the performance of the YOLOv5 model, we found that YOLOv5s performed better than YOLOv5x in terms of overall performance, with the best performance at an image resolution of 640×640 (mAP of 96.8%, FPS of 71.43). And the comparison with the latest YOLOv8s showed that the YOLOv5s performed better than the YOLOv8s. Finally, the trained model was deployed to the Android mobile, and the results showed that mobile-side detection was the best when the image resolution was 256×256, with an accuracy of 81.0% and FPS of 6.98. The real-time recognition system established in this study can provide technical support for infestation forecasting and precise prevention of A. gossypii.

Keywords: Aphis gossypii Glover; Faster R-CNN; SSD; YOLOv5; deep learning.

Grants and funding

This study was supported by the Xinjiang Joint Funds of the National Natural Science Foundation of China (U2003119) and China National Key R&D Program during the 14th Five-year Plan Period (2022YFD1400302).