Performance Analysis of YOLOv3 for Real-Time Detection of Pests in Soybeans
In this work, we evaluate the You Only Look Once (YOLOv3) architecture for real-time detection of insect pests in soybean. Soybean crop images were collected on different days, locations, and weather conditions between the phenological stages R1 to R6, considered the period of the high occurrence of soybean pests. For training and testing the neural network, we used 5-fold cross-validation analyzing four metrics to evaluate the classification results: precision, recall, F-score, and accuracy; and three metrics to evaluate the detection results: mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2). The experimental results showed that the YOLOv3 architecture trained with batch size 32 leads to higher classification and detection rates than batch sizes 4 and 16. The results indicate that the evaluated architecture can support experts and farmers in monitoring pest control action levels in soybean fields.