您好，欢迎访问新疆农业科学院 机构知识库！
新疆农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

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

文献类型： 外文期刊

作者： Xu, Xin 1 ; Shi, Jing 1 ; Chen, Yongqin 1 ; He, Qiang 1 ; Liu, Liangliang 1 ; Sun, Tong 1 ; Ding, Ruifeng 2 ; Lu, Yanhui 3 ; Xue, Chaoqun 4 ; Qiao, Hongbo 1 ;

作者机构： 1.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou, Peoples R China

2.Xinjiang Acad Agr Sci, Inst Plant Protect, Urumqi, Peoples R China

3.Chinese Acad Agr Sci, Inst Plant Protect, Beijing, Peoples R China

4.China Natl Tobacco Corp CNTC, Zhengzhou Tobacco Res Inst, Zhengzhou, Peoples R China

关键词： Aphis gossypii Glover; Faster R-CNN; YOLOv5; SSD; deep learning

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.6； 五年影响因子：6.8 ）

ISSN： 1664-462X

年卷期： 2023 年 14 卷

页码：

收录情况： SCI

摘要： 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 640x640 (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 256x256, 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.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：新疆农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台