AUTHOR=He Junjie , Zhang Shihao , Yang Chunhua , Wang Houqiao , Gao Jun , Huang Wei , Wang Qiaomei , Wang Xinghua , Yuan Wenxia , Wu Yamin , Li Lei , Xu Jiayi , Wang Zejun , Zhang Rukui , Wang Baijuan 

TITLE=Pest recognition in microstates state: an improvement of YOLOv7 based on Spatial and Channel Reconstruction Convolution for feature redundancy and vision transformer with Bi-Level Routing Attention

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1327237

DOI=10.3389/fpls.2024.1327237

ISSN=1664-462X

ABSTRACT=In order to solve the problem of precise identification and counting of tea pests, this study proposes has proposed a novel tea pest identification method based on improved YOLOv7 network. This method uses used MPDIou to optimize the original loss function, which improves improved the convergence speed of the model and simplifies the calculation process. Replace part of the network structure of the original model using Spatial and Channel reconstruction Convolution to reduce redundant features, lower the complexity of the model, and reduce computational costs. The Vision Transformer with Bi-Level Routing Attention has been incorporated to enhance the flexibility of model calculation allocation and content perception. The experimental resultsreveal revealed that the enhanced YOLOv7 model significantly boosts boosted Precision, Recall, F1, and mAP by 5.68%, 5.14%, 5.41%, and 2.58% respectively, compared to the original YOLOv7. Furthermore, when compared to deep learning networks such as SSD, Faster RCNN, and the original YOLOv7, this method proves to be superior while being externally validated. It exhibits exhibited a noticeable improvement in the FPS rates, with increments of 5.75 HZ, 34.42 HZ, and 25.44 HZ respectively. Moreover, the mAP for actual detection experiences significant enhancements, with respective increases of 2.49%, 12.26%, and 7.26%.Additionally, the parameter size is reduced by 1.39 G relative to the original model. The improved model can not only identify and count tea pests efficiently and accurately, but also has the characteristics of high recognition rate, low parameters and high detection speed. It is of great significance to achieve realize the intelligent and precise prevention and control of tea pests.