Transfer Learning-Based Multi-Class Pest Detection using a Real-Time Object Detection Framework with Control-Oriented Grouping for Smart Agriculture

Pest infestations cause annual crop yield losses of 20–40% and over USD 220 billion in damages, with smallholder farmers most affected. Conventional monitoring methods are labor-intensive, subjective, and slow, motivating automated, real-time approaches. This study proposes a control-oriented pest detection system using YOLO11, trained on 14 agriculturally significant pest classes and extended with a post-hoc grouping mechanism aligned to Integrated Pest Management (IPM). The model achieved a mean precision of 0.775, recall of 0.601, AP50 of 0.700, and mAP50–95 of 0.553, with an overall mAP@0.5 of 0.704. Strong results were observed for tomato_hornworms (0.838), armyworm (0.829), and brown_marmorated_stink_bug…