ArticlePlant methods2025
Comparison of YOLO-based sorghum spike identification detection models and monitoring at the flowering stage.
Article in Plant methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- MOSSNet: multiscale and oriented sorghum spike detection and counting in UAV images.Frontiers in plant science · 2025Article
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10 authors.
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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
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