PestLite: A Novel YOLO-Based Deep Learning Technique for Crop Pest Detection

Qing Dong, Lina Sun, Tianxin Han (), Minqi Cai and Ce Gao
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Qing Dong: Department of Process Equipment and Control Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Lina Sun: Department of Process Equipment and Control Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Tianxin Han: Department of Process Equipment and Control Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Minqi Cai: Institute of Insect Research, College of Animal Science and Technology, Yangzhou University, Yangzhou 225000, China
Ce Gao: School of Economics and Business Administration, Beijing Normal University, Beijing 100875, China

Agriculture, 2024, vol. 14, issue 2, 1-30

Abstract: Timely and effective pest detection is essential for agricultural production, facing challenges such as complex backgrounds and a vast number of parameters. Seeking solutions has become a pressing matter. This paper, based on the YOLOv5 algorithm, developed the PestLite model. The model surpasses previous spatial pooling methods with our uniquely designed Multi-Level Spatial Pyramid Pooling (MTSPPF). Using a lightweight unit, it integrates convolution, normalization, and activation operations. It excels in capturing multi-scale features, ensuring rich extraction of key information at various scales. Notably, MTSPPF not only enhances detection accuracy but also reduces the parameter size, making it ideal for lightweight pest detection models. Additionally, we introduced the Involution and Efficient Channel Attention (ECA) attention mechanisms to enhance contextual understanding. We also replaced traditional upsampling with Content-Aware ReAssembly of FEatures (CARAFE), which enable the model to achieve higher mean average precision in detection. Testing on a pest dataset showed improved accuracy while reducing parameter size. The mAP50 increased from 87.9% to 90.7%, and the parameter count decreased from 7.03 M to 6.09 M. We further validated the PestLite model using the IP102 dataset, and on the other hand, we conducted comparisons with mainstream models. Furthermore, we visualized the detection targets. The results indicate that the PestLite model provides an effective solution for real-time target detection in agricultural pests.

Keywords: pest detection; agricultural production; real-time target detection; MTSPPF; ECA; involution; CARAFE; YOLOv5; IP102 (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: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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