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

• Published in: Agriculture (Basel) Vol. 14; no. 2; p. 228
• Main Authors: Dong, Qing, Sun, Lina, Han, Tianxin, Cai, Minqi, Gao, Ce
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: Accuracy; Agricultural pests; Agricultural production; Agriculture; Algorithms; Corn; Crops; data collection; Datasets; Deep learning; Design; ECA; Efficiency; involution; Kitchenware; Mathematical models; Methods; Morphology; MTSPPF; Neural networks; Object recognition; Parameters; pest detection; Pests; plant pests; real-time target detection; Rice; Target detection

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 pr...