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

文献类型： 外文期刊

作者： Zhang, Song 1 ; Yang, Yehua 4 ; Tu, Lei 1 ; Fu, Tianling 2 ; Chen, Shenxi 1 ; Cen, Fulang 1 ; Yang, Sanwei 1 ; Zhao, Quanzhi 1 ; Gao, Zhenran 1 ; He, Tengbing 1 ;

作者机构： 1.Guizhou Univ, Coll Agr, Guiyang 550025, Peoples R China

2.Guizhou Univ, Inst Rural Revitalizat, Guiyang 550025, Peoples R China

3.Guiding Agr & Rural Bur, Guiding 551300, Peoples R China

4.Guizhou Acad Agr Sci, Inst Soil & Fertilizer, Guiyang 550025, Peoples R China

关键词： UAV; Sorghum; Spike identification; Flowering stage monitoring; YOLO

期刊名称：PLANT METHODS （ 影响因子：4.4； 五年影响因子：5.7 ）

ISSN：

年卷期： 2025 年 21 卷 1 期

页码：

收录情况： SCI

摘要： 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 15sunny and 15cloudy 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.

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