Comparison of YOLO-based sorghum spike identification detection models and monitoring at the flowering stage.

Abstract

Monitoring sorghum during the flowering stage is essential for effective fertilization management and improving yield quality, with spike identification serving as the core component of this process. Factors such as varying heights and weather conditions significantly influence the accuracy of sorghum spike detection models, and few comparative studies exist on model performance under different conditions. YOLO (You Only Look Once) is a deep learning object detection algorithm. In this research, images of sorghum during the flowering stage were captured at two heights (15 m and 30 m) in 2023 via a UAV and utilized to train and evaluate variants of YOLOv5, YOLOv8, YOLOv9, and YOLOv10. This investigation aimed to assess the impact of dataset size on model accuracy and predict sorghum flowering stages. The results indicated that YOLOv5, YOLOv8, YOLOv9, and YOLOv10 achieved mAP@50 values of 0.971, 0.968, 0.967, and 0.965, respectively, with dataset sizes ranging from 200 to 350. YOLOv8m performed best on 15_sunny and 15_cloudy clouds and, overall, exhibited superior adaptability and generalizability. The predictions of the flowering stage using YOLOv8m were more accurate at heights between 12 and 15 m, with R² values ranging from 0.88 to 0.957 and rRMSE values between 0.111 and 0.396. This research addresses a significant gap in the comparative evaluation of models for sorghum spike detection, identifies YOLOv8m as the most effective model, and advances flowering stage monitoring. These findings provide theoretical and technical foundations for the application of YOLO models in sorghum spike detection and flowering stage monitoring. These findings provide a technical means for the timely and efficient management of sorghum flowering.