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Detecting Pests From Light-Trapping Images Based on Improved YOLOv3 Model and Instance Augmentation.

Jiawei Lv^1,2,3, Wenyong Li^1,2, Mingyuan Fan³

¹National Engineering Research Center for Information Technology in Agriculture, Beijing, China.

Frontiers in Plant Science

|July 25, 2022

Summary

This study introduces an enhanced YOLOv3 model for improved detection of maize pests from light trap images. The model addresses challenges like class imbalance and target occlusion, achieving superior performance in agricultural environments.

Keywords:

YOLOv3 convolutional neural network image cropping pest detection pests and diseases

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Area of Science:

Agricultural Entomology
Computer Vision
Machine Learning

Background:

Light traps are crucial for monitoring agricultural and forest insect pests.
Current pest detection methods face limitations including imbalanced data, target occlusion, and inter-species similarity.

Purpose of the Study:

To develop an improved YOLOv3 model for accurate detection of crop pests in agricultural settings.
To enhance pest monitoring capabilities using advanced image processing and deep learning techniques.

Main Methods:

Constructed a dataset of nine common maize pests with image augmentation.
Implemented an improved YOLOv3 model incorporating optimized anchors via linear transformation and modified residual blocks.
Integrated image enhancement techniques to address detection challenges.

Main Results:

The proposed YOLOv3 model achieved a 6.3% increase in mean Average Precision (mAP) and a 4.61% increase in mean Recall (mRecall) compared to the original YOLOv3.
Demonstrated superior performance over state-of-the-art methods like SSD, Faster R-CNN, and YOLOv4.

Conclusions:

The enhanced YOLOv3 model offers significant improvements in detecting crop pests, particularly maize pests.
This model provides an effective solution for intelligent monitoring of agricultural pests, overcoming existing detection limitations.