Computer vision presents a great opportunity for improving pest monitoring in agriculture, particularly for yellow sticky traps, a critical component in IPM. However, despite the growing interest in applying object detection models for insect identification, insect datasets present unique challenges, and approaches for fine-tuning model parameters to achieve reliable performance remain limited. This study explores the influence of fine tuning three key hyperparameters (learning rate, optimizer type and batch size) on the performance of two popular object detection models (YOLO and RT-DETR), in detecting pests on yellow sticky traps, with a particular emphasis on identifying cucumber beetles. Results showed that higher learning rates reduced performance across precision, recall, and mAP50 for both models. In contrast, SGD improved outcomes, particularly for RT-DETR, while YOLO proved more robust to high learning rates. Our study also showed that both models achieved comparable accuracy levels, once optimal settings were determined for each model. These findings highlight the importance of hyperparameter tuning for reliable pest detection systems and support the development of scalable AI workflows for precision agriculture.
Mafuwe et al. (Thu,) studied this question.