UAV-based estimation of post-sowing rice plant density using RGB imagery and deep learning across multiple altitudes

Abstract

This study presents a novel and efficient approach to accurately assess post-sowing rice plant density by leveraging unmanned aerial vehicles (UAVs) equipped with high-resolution RGB cameras. In contrast to labor-intensive and spatially limited traditional methods that rely on manual sampling and extrapolation, our proposed methodology uses UAVs to rapidly and comprehensively survey entire paddy fields at optimized altitudes (4, 6, 8, and 10 m). Aerial imagery was autonomously acquired 17 days post-sowing, following a pre-defined flight path. The robust rice plant density estimation process incorporates two key innovations: first, a dynamic system of 12 adaptive segmentation thresholding blocks that effectively detects rice seed presence across diverse and variable background conditions. Second, a tailored three-layer convolutional neural network (CNN) accurately classifies vegetative situations. To maximize the training efficiency and performance, we implemented both a pretrained model and a deep learning model, conducting a rigorous comparative analysis against the state-of-the-art YOLOv10. Notably, under favorable imaging conditions, our findings indicate that a 6-m flight altitude yields optimal results, achieving a high degree of accuracy with rice plant density estimates that closely align with those obtained through traditional ground-based methods. This investigation unequivocally highlights the significant advantages of UAV-based monitoring as an economically viable, spatially comprehensive, and demonstrably accurate tool for precise rice field management, ultimately contributing to enhanced crop yields, improved food security, and the promotion of sustainable agricultural practices.

Plant phenotyping relevance

UAV RGB画像と適応的セグメンテーション、CNNを用いてイネ個体密度を推定する手法を開発・比較・検証しており、植物形質の取得が研究の中心です。

Code and data availability

The paper's data availability statement points to a public Zenodo deposit containing the study's datasets (UAV RGB imagery/labels used for rice plant density estimation). No separate author analysis code repository is stated.