1. Crop Insect Identification Based on Improved YOLOv7.
- Author
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HUANG Shirui, WANG Tianyi, WEN Tao, and ZHOU Jianglong
- Subjects
OBJECT recognition (Computer vision) ,AGRICULTURAL pests ,PEST control ,IMAGE processing ,AGRICULTURAL implements - Abstract
In order to solve the problem of time-consuming and laborious manual detection of crop pests, a crop pest recognition model based on YOLOv7 was proposed in this paper. Firstly, the feature fusion module of YOLOv7 was improved using the information aggregation-distribution mechanism, which enhanced the feature fusion ability between different levels. Secondly, the loss function was replaced by minimum points distance intersection over union to calculate the boundary box regression loss, which better aligned the predicted box and the real target box, and improved the accuracy of the boundary box regression. Finally, the receptive field enhancement module was added after the SPPCSPC layer to enhance the recognition ability of the model to small-scale pests. Experimental results showed that the average accuracy of the improved YOLOv7 model was 80.4%, the precision rate was 85.3%, and the recall rate was 75.1%, which were 3.4%, 3.2% and 2.6% higher than those before improvement. The model had better recognition effect and robustness for agricultural pests, and provided a more accurate and reliable tool for agricultural pest monitoring and control. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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