Li Haixia, Li Qian, Yu Chunlai, Luo Shanjun
Huanghe University of Science and Technology, Zhengzhou, 450006, China.
Aerospace Information Research Institute, Henan Academy of Sciences, Zhengzhou, 450046, China.
Plant Methods. 2025 May 30;21(1):73. doi: 10.1186/s13007-025-01398-1.
Rice is one of the major food crops in the world, and the monitoring of its growth condition is of great significance for guaranteeing food security and promoting sustainable agricultural development. Leaf area index (LAI) is a key indicator for assessing the growth condition and yield potential of rice, and the traditional methods for obtaining LAI have problems such as low efficiency and large error. With the development of remote sensing technology, unmanned aerial multispectral remote sensing combined with deep learning technology provides a new way for efficient and accurate estimation of LAI in rice.
In this study, a multispectral camera mounted on a UAV was utilized to acquire rice canopy image data, and rice LAI was uniformly estimated over multiple periods by the multilayer perceptron (MLP) and convolutional neural network (CNN) models in deep learning. The results showed that the CNN model based on five-band reflectance images (490, 550, 670, 720, and 850 nm) as input after feature screening exhibited high estimation accuracy at different growth stages. Compared with the traditional MLP model with multiple vegetation indices as inputs, the CNN model could better process the original multispectral image data, effectively avoiding the problem of vegetation index saturation, and improving the accuracies by 4.89, 5.76, 10.96, 1.84 and 6.01% in the rice tillering, jointing, booting, and heading periods, respectively, and the overall accuracy was improved by 6.01%. Moreover, the model accuracies (MLP and CNN) before and after variable screening showed noticeable changes. Conducting variable screening contributed to a substantial improvement in the accuracy of rice LAI estimation.
UAV multispectral remote sensing combined with CNN technology provides an efficient and accurate method for the unified multi-period estimation of rice LAI. Moreover, the generalization ability and adaptability of the model were further improved by rational variable screening and data enhancement techniques. This study can provide a technical support for precision agriculture and a more accurate solution for rice growth monitoring. More feature extraction and variable screening methods can be further explored in future studies by optimizing the model structure to improve the accuracy and stability of the model.
水稻是世界主要粮食作物之一，监测其生长状况对保障粮食安全和促进农业可持续发展具有重要意义。叶面积指数（LAI）是评估水稻生长状况和产量潜力的关键指标，传统获取LAI的方法存在效率低、误差大等问题。随着遥感技术的发展，无人机多光谱遥感结合深度学习技术为水稻LAI的高效准确估算提供了新途径。
本研究利用搭载在无人机上的多光谱相机获取水稻冠层图像数据，并通过深度学习中的多层感知器（MLP）和卷积神经网络（CNN）模型对多个时期的水稻LAI进行统一估算。结果表明，基于经特征筛选后的五波段反射率图像（490、550、670、720和850nm）作为输入的CNN模型在不同生长阶段均表现出较高的估算精度。与以多种植被指数为输入的传统MLP模型相比，CNN模型能更好地处理原始多光谱图像数据，有效避免植被指数饱和问题，在水稻分蘖期、拔节期、孕穗期和抽穗期的估算精度分别提高了4.89%、5.76%、10.96%、1.84%和6.01%，总体精度提高了6.01%。此外，变量筛选前后模型（MLP和CNN）的精度有显著变化。进行变量筛选有助于大幅提高水稻LAI估算的精度。
无人机多光谱遥感结合CNN技术为水稻LAI的统一多时期估算提供了高效准确的方法。此外，通过合理的变量筛选和数据增强技术进一步提高了模型的泛化能力和适应性。本研究可为精准农业提供技术支持，为水稻生长监测提供更准确的解决方案。未来研究可通过优化模型结构进一步探索更多特征提取和变量筛选方法，以提高模型的精度和稳定性。
