Estimation of sorghum seedling number from drone image based on support vector machine and YOLO algorithms.

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 R 2 values of the model outputs for images captured at heights of 15 m, 30 m, and 45 m were, respectively, (SVM: 0.67, 0.57, 0.51), (YOLOv5: 0.76, 0.57, 0.56), and (YOLOv8: 0.93, 0.90, 0.71). Therefore, the YOLOv8 model was most accurate in estimating the number of sorghum seedlings. The results indicate that UAV images combined with an appropriate model can be effective for large-scale counting of sorghum seedlings. This method will be a useful tool for sorghum phenotyping.

Plant phenotyping relevance

UAV画像からソルガム幼苗数という植物形質を自動推定するモデルを開発・比較し、異なる飛行高度で頑健性を検証しており、表現型取得手法が中心である。

Code and data availability

The supplied article blocks describe UAV RGB imagery of sorghum seedlings, a labeled dataset (108 images, 12,755 seedlings), and SVM/YOLOv5/YOLOv8 models, but contain no data availability statement, repository deposit, or authors' public URL for the images, annotations, code, or trained models. No paper-specific public

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