ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Computer Vision
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1551326
UAV-Based Estimation of Post-Sowing Rice Plant Density Using RGB Imagery and Deep Learning across Multiple Altitudes
Provisionally accepted
- 1Can Tho University, Can Tho, Vietnam
- 2Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam
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This study introduces 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 relying on manual sampling and extrapolation, our proposed methodology employs UAVs to rapidly and comprehensively survey entire paddy fields at optimized altitudes (4m, 6m, 8m, and 10m). Aerial imagery was autonomously acquired 17 days post-sowing following a pre-defined flight path. The robust rice plant density estimation process integrates two key innovations: first, a dynamic system of twelve adaptive segmentation thresholding blocks effectively distinguishes rice seed presence across diverse and variable background conditions. Second, a tailored three-layer Convolutional Neural Network (CNN) accurately classifies vegetative situations. To maximize 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 demonstrate that a 6-meter 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
Keywords: UAV, YOLOv10, precision agriculture, rice plant density, deep learning
Received: 25 Dec 2024; Accepted: 16 Jun 2025.
Copyright: © 2025 Hieu Luu, Ngo, Nguyen, Nguyen and Phan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Trong Hieu Luu, Can Tho University, Can Tho, Vietnam
Quang Hieu Ngo, Can Tho University, Can Tho, Vietnam
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