ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1636967
This article is part of the Research TopicPlant Phenotyping for AgricultureView all 6 articles
Estimating the full-period rice leaf area index using CNN-LSTM-Attention and multispectral images from unmanned aerial vehicles
Provisionally accepted- 1Huanghe University of Science and Technology, Zhengzhou, China
- 2Henan Academy of Sciences, Zhengzhou, China
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Introduction: Leaf area index (LAI) of rice is a crucial parameter for assessing the growth conditions and predicting yields. However, traditional measurement methods are inefficient and insufficient for large-scale monitoring.This study proposes a CNN-LSTM-Attention (CLA) model that integrates convolutional neural networks (CNN), long short-term memory (LSTM), and a self-attention mechanism, aiming to achieve high-precision estimation of rice LAI across all growth stages based on the unmanned aerial vehicle (UAV) multispectral imagery and deep learning techniques. The estimation performance of vegetation indices (VIs), machine learning methods (SVR, RFR, PLSR, XGBoost), and deep learning models (DNN, CNN, LSTM) were comparatively analyzed.The results show that the CLA model outperforms other approaches in estimating rice LAI throughout the entire growing period, achieving a coefficient of determination (R² ) of 0.92 and a relative root mean square error (RRMSE) below 9%, significantly better than linear regression and machine learning methods. Moreover, the CLA model maintains high stability and accuracy across different LAI ranges, with notably reduced errors for low LAI values (one to three), effectively mitigating the influence of soil background. This research offers an efficient and accurate technological approach for rice growth monitoring and holds significant implications for precision agricultural management.
Keywords: LAI, UAV, multispectral imagery, deep learning, remote sensing
Received: 28 May 2025; Accepted: 04 Aug 2025.
Copyright: © 2025 Li, Yue and Luo. 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: Shanjun Luo, Henan Academy of Sciences, Zhengzhou, China
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