ArticleFrontiers in plant science2024
Estimation of sorghum seedling number from drone image based on support vector machine and YOLO algorithms.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Automated Rice Seedling Segmentation and Unsupervised Health Assessment Using Segment Anything Model with Multi-Modal Feature Analysis.Sensors (Basel, Switzerland) · 2025Article
- MOSSNet: multiscale and oriented sorghum spike detection and counting in UAV images.Frontiers in plant science · 2025Article
- YOLOv8s-Longan: a lightweight detection method for the longan fruit-picking UAV.Frontiers in plant science · 2024Article
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9 authors.
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Abstract
Accurately counting the number of sorghum seedlings from images captured by unmanned aerial vehicles (UAV) is useful for identifying sorghum varieties with high seedling emergence rates in breeding programs. The traditional method is manual counting, which is time-consuming and laborious. Recently, UAV have been widely used for crop growth monitoring because of their low cost, and their ability to collect high-resolution images and other data non-destructively. However, estimating the number of sorghum seedlings is challenging because of the complexity of field environments. The aim of this study was to test three models for counting sorghum seedlings rapidly and automatically from red-green-blue (RGB) images captured at different flight altitudes by a UAV. The three models were a machine learning approach (Support Vector Machines, SVM) and two deep learning approaches (YOLOv5 and YOLOv8). The robustness of the models was verified using RGB images collected at different heights. The
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