Tea Tree Pest Detection Algorithm Based on Improved Yolov7-Tiny

Zijia Yang, Hailin Feng (), Yaoping Ruan and Xiang Weng
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Zijia Yang: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Hailin Feng: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Yaoping Ruan: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Xiang Weng: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Lin′an, Hangzhou 311300, China

Agriculture, 2023, vol. 13, issue 5, 1-22

Abstract: Timely and accurate identification of tea tree pests is critical for effective tea tree pest control. We collected image data sets of eight common tea tree pests to accurately represent the true appearance of various aspects of tea tree pests. The dataset contains 782 images, each containing 1~5 different pest species randomly distributed. Based on this dataset, a tea garden pest detection and recognition model was designed using the Yolov7-tiny network target detection algorithm, which incorporates deformable convolution, the Biformer dynamic attention mechanism, a non-maximal suppression algorithm module, and a new implicit decoupling head. Ablation experiments were conducted to compare the performance of the models, and the new model achieved an average accuracy of 93.23%. To ensure the validity of the model, it was compared to seven common detection models, including Efficientdet, Faster Rcnn, Retinanet, DetNet, Yolov5s, YoloR, and Yolov6. Additionally, feature visualization of the images was performed. The results demonstrated that the Improved Yolov7-tiny model developed was able to better capture the characteristics of tea tree pests. The pest detection model proposed has promising application prospects and has the potential to reduce the time and economic cost of pest control in tea plantations.

Keywords: Yolov7-tiny; DCNv3; Biformer; tea tree pest identification; soft-NMS (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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