Improved YOLOv8 and SAHI Model for the Collaborative Detection of Small Targets at the Micro Scale: A Case Study of Pest Detection in Tea

• Published in: Agronomy (Basel) Vol. 14; no. 5; p. 1034
• Main Authors: Ye, Rong, Gao, Quan, Qian, Ye, Sun, Jihong, Li, Tong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Accuracy; Agricultural production; Agriculture; agronomy; Algorithms; BiFormer; Case studies; Computational linguistics; Crop diseases; Deep learning; Feature extraction; GELAN; Image enhancement; Image resolution; Insects; Language processing; lighting; Machine learning; model validation; Natural language interfaces; neck; Pests; SAHI; Semantics; small object detection; Target detection; Target recognition; Tea; tea pest damage; YOLOv8; China

Pest target identification in agricultural production environments is challenging due to the dense distribution, small size, and high density of pests. Additionally, changeable environmental lighting and complex backgrounds further complicate the detection process. This study focuses on enhancing th...