Deshan Huang , Yuandong Hong , Jianquan Li

Hier Rice Research Center, Hainan Institute of Tropical Agricultural Resources, Sanya, 572025, Hainan, China
Author    Correspondence author
Field Crop, 2025, Vol. 8, No. 4   
Received: 17 Jun., 2025    Accepted: 29 Jul., 2025    Published: 20 Aug., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

As one of the most important food crops in the world, the production level of rice is directly related to food security and sustainable agricultural development. The growth monitoring of rice in the field environment is confronted with challenges such as wide distribution of fields, significant environmental differences and complex growth processes. Unmanned aerial vehicle (UAV) remote sensing technology, with its high spatial resolution and flexibility, provides a new approach for obtaining growth parameters of rice populations. Meanwhile, machine learning methods have significant advantages in multi-source data processing, feature extraction and pattern recognition. This study explores the integrated framework of unmanned aerial vehicle (UAV) remote sensing and machine learning, summarizes the application characteristics of various sensors (RGB, multispectral, hyperspectral, and thermal infrared), assesses the applicability of vegetation index, canopy structure parameters, and physiological and ecological indicators in rice growth monitoring, and analyzes the performance of models such as random forest, support vector machine, XGBoost, and deep learning The application potential of this technology in the prediction of rice yield and disease monitoring in the Yangtze River Basin of China and Southeast Asia was demonstrated through case studies. The research results show that the combination of UAV and machine learning can effectively achieve precise monitoring of large-scale rice growth, which is of great significance to the development of precision agriculture and smart agriculture. This study aims to construct a monitoring framework integrating unmanned aerial vehicle (UAV) remote sensing and machine learning to achieve dynamic, precise and large-scale assessment of the growth status of rice in the field.

Rice; Unmanned aerial vehicle remote sensing; Machine learning; Growth monitoring; Precision agriculture