智能化农业装备学报（中英文）2025，Vol.6Issue(2)：97-104,8.DOI:10.12398/j.issn.2096-7217.2025.02.009
基于随机森林及遥感植被指数的无人农场水稻产量预测研究
Rice yield prediction of unmanned farm based on random forest and remote sensing vegetation index
摘要
Abstract
In order to ensure national food security and promote sustainable agricultural development,it is crucial to accurately predict regional rice yield by formulating effective food strategies.While advancemnets have been achieved by integrating remote sensing data with machine learning technology for yield prediction,a more in-depth analysis of the machine learning model mechanism and the application of high spatio-temporal resolution data remains necessary.This study utilized Sentinel and MODIS Normalized Difference Vegetation Index(NDVI)data along with county-level yield statistics from 2023 to implement a Random Forest(RF)model for predicting yield from county level to pixel level with case study of Sheyang unmanned farm in Jiangsu Province,and investigated the impact of different features on the model's learning mechanism.The results demonstrated that there was a significant correlation between NDVI data and rice yield during the period of August to October 2023,which is a critical period in the crop phenological cycle.The RF model effectively predicted rice yield at the county level,with a Root Mean Square Error(RMSE)value of 339.5 kg/hm2.Furthermore,spatial distribution mapping of the predicted yields within Sheyang County for 2023 revealed significant heterogeneity,indicating lower yields in marginal regions and higher yields in central areas ranging from 9 000 to 9 300 kg/hm2.This study not only enhances understanding of machine learning application and vegetation data in yield prediction but also provides theoretical support for improving model accuracy and formulating scientific food production strategies.关键词
随机森林/机器学习/遥感/植被指数/产量预测/生育期Key words
Random Forest/machine learning/remote sensing/vegetation index/yield forecast/growth stage分类
农业科技引用本文复制引用
王俊,吴振伟,姜海,柯娟,FOYEZ Ahmed Prodhan..基于随机森林及遥感植被指数的无人农场水稻产量预测研究[J].智能化农业装备学报（中英文）,2025,6(2):97-104,8.基金项目
国家自然科学基金(42071425) （42071425）
江苏常州钟楼区揭榜挂帅项目(JBGS2023011) National Natural Science Foundation of China(42071425) （JBGS2023011）
Jiangsu Changzhou Zhonglou District Challenge-Response System Project(JBGS2023011) （JBGS2023011）
