From canopy segmentation to accurate prediction: An UAV-based multi-feature fusion framework for plot-scale ratoon sugarcane seedling counting

Abstract

• Proposes a UAV remote sensing framework for ratoon sugarcane seedling counting. • Achieves precise segmentation of canopy using unsupervised learning methods. • Integrates multiple feature methods and models; KBest-F with GBR excels (R 2 = 0.7641). • Reveals the core feature contribution mechanism based on SHAP analysis. • Compares deep learning methods and proves the reliability of the work. Accurate and efficient monitoring of seedling emergence is critical for early-stage crop management and yield forecasting in sugarcane production. To meet this practical demand for precise field phenotyping, this study developed a high-throughput phenotyping framework leveraging unmanned aerial vehicle (UAV) remote sensing data and machine learning. This framework addresses the critical agricultural challenges of inefficient manual counting and the need for plot-scale monitoring in sugarcane production by enabling high-throughput sugarcane seedling number prediction through the integration of UAV-acquired RGB and multispectral imagery. Specifically, the sugarcane canopy was accurately segmented from the background using K-means clustering, a step that enabled the extraction of canopy area and the generation of a mask for obtaining canopy-level average features (including vegetation indices and texture features). These features together form a comprehensive feature set. Subsequently, six different feature selection methods were used to optimize the feature set, and eight machine learning models were combined for training and evaluation. The results showed that the combination of Gradient Boosting Regression (GBR) and KBest-F feature selection method yielded the optimal prediction performance, with a coefficient of determination (R 2 ) of 0.7641, a root mean square error (RMSE) of 19.42, and a mean absolute error (MAE) of 15.93. Further analysis identified canopy area, the Normalized Difference Red Edge Index (NDRE), red edge contrast, and green entropy as core predictive features. They collectively contribute over 60% of total feature importance, and their synergistic effects support accurate seedling number estimation. This framework offers an efficient, scalable tool for plot-scale seedling monitoring, with substantial potential for precision field management of high-density crops.

Plant phenotyping relevance

UAV画像、キャノピー segmentation、特徴抽出、機械学習を統合し、サトウキビ苗数という植物状態を圃場スケールで推定する高スループット表現型計測フレームワークが研究の中心である。

Code and data availability

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