A YOLOv7 incorporating the Adan optimizer based corn pests identification method

Frontiers in Plant Science

Plant Science

Yaochi Zhao¹  Lewei Xu²  Chong Zhang²  Zhuhua Hu² 

[1] School of Cyberspace Security, State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, China;School of Information and Communication Engineering, State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, China;

关键词: YOLOv7;    smart agriculture;    object detection;    deep learning;    pests identification;   

DOI  :  10.3389/fpls.2023.1174556

 received in 2023-02-26, accepted in 2023-05-02,  发布年份 2023

来源: Frontiers

【 摘 要 】

Major pests of corn insects include corn borer, armyworm, bollworm, aphid, and corn leaf mites. Timely and accurate detection of these pests is crucial for effective pests control and scientific decision making. However, existing methods for identification based on traditional machine learning and neural networks are limited by high model training costs and low recognition accuracy. To address these problems, we proposed a YOLOv7 maize pests identification method incorporating the Adan optimizer. First, we selected three major corn pests, corn borer, armyworm and bollworm as research objects. Then, we collected and constructed a corn pests dataset by using data augmentation to address the problem of scarce corn pests data. Second, we chose the YOLOv7 network as the detection model, and we proposed to replace the original optimizer of YOLOv7 with the Adan optimizer for its high computational cost. The Adan optimizer can efficiently sense the surrounding gradient information in advance, allowing the model to escape sharp local minima. Thus, the robustness and accuracy of the model can be improved while significantly reducing the computing power. Finally, we did ablation experiments and compared the experiments with traditional methods and other common object detection networks. Theoretical analysis and experimental result show that the model incorporating with Adan optimizer only requires 1/2-2/3 of the computing power of the original network to obtain performance beyond that of the original network. The mAP@[.5:.95] (mean Average Precision) of the improved network reaches 96.69% and the precision reaches 99.95%. Meanwhile, the mAP@[.5:.95] was improved by 2.79%-11.83% compared to the original YOLOv7 and 41.98%-60.61% compared to other common object detection models. In complex natural scenes, our proposed method is not only time-efficient and has higher recognition accuracy, reaching the level of SOTA.

【 授权许可】

Unknown   
Copyright © 2023 Zhang, Hu, Xu and Zhao