Pest recognition model of cultivated alfalfa based on deep learning

Alfalfa (Medicago sativa) is a high-quality forb that is important for the development of animal husbandry in China. Pests and diseases are the main factors affecting alfalfa growth and quality; Therefore, accurate identification of these pests is crucial to alfalfa cultivation. One-stage object detection algorithms such as YOLO (You Only Look Once) performs target detection from end to end and the two-stage target detection algorithm based on RCNN (Region Convolutional Neural Network) generates candidate regions for feature extraction. To effectively identify alfalfa pests, YOLOv5 and Faster-RCNN are used to identify six common pests of alfalfa based on feature recognition in this study. The optimal algorithm and model for identifying alfalfa pests are determined based on four evaluation metrics: Recall, Precision, mAP, and F1 score. Recall is the proportion of positive cases in the sample that are correctly predicted, and F1 is the weighted average of R and P. The results Recall that YOLOv5 outperformed Faster-RCNN in identifying alfalfa pests, with higher precision scores on both the testing and validation sets. This study provides scientific and theoretical support for the selection of algorithms for identifying alfalfa pests, which has important implications for alfalfa cultivation management.