灌溉排水学报2025，Vol.44Issue(1)：43-49,65,8.DOI:10.13522/j.cnki.ggps.2024218
基于无人机遥感的春玉米产量预测研究
Predicting spring maize yield using UAV remote sensing
摘要
Abstract
[Objective]Predicting potential crop yield is critical not only to policy making but also to improving agricultural management to safeguard food production.In this paper,we investigate the feasibility of using unmanned aerial vehicles to predict crop yields.[Method]Spring maize was used as the model plant.The field experiment consisted of five soil moisture treatments by irrigating 50%(W1),75%(W2),100%(W3),125%(W4)and 150%(W5)of estimated evapotranspiration.Multispectral images captured by an unmanned aerial vehicle(UAV)were used to construct the vegetation indices,and the pearson's correlation coefficient method was used to screen the input variables for the yield prediction models.Partial least squares(PLS),random forest regression(RF),and particle swarm optimization(PSO)were used to develop and optimize yield prediction models based on the UAV images obtained at the nodulation,tasseling and filling stages of the maize growth.The grain yield was predicted using both single vegetation index and multi-vegetation indices,with the most accurate model used to generate a yield map of the studied area.[Result]Models using multiple vegetation indices were more accurate than models using single vegetation index.Among all models,the PSO-optimized random forest(PSO-RF)model was most accurate during the tasseling stage.Compared with the measured data,the PSO-RF model based on NDVI achieved an R2 of 0.685,RMSE of 1 792.71 kg/hm2,and RPD of 1.764,when using single index,while when using multi-indices,it achieved an R2 of 0.806,RMSE of 1 485.88 kg/hm2,and RPD of 2.032.The W3 produced the highest grain yield(19 845.25 kg/hm2),while the W1 resulted in the least yield(12 054.52 kg/hm2).[Conclusion]Multispectral UAV imagery combined with PSO-optimized random forest models offers a robust and accurate method for crop yield prediction.This method can serve as a valuable tool to improve agricultural management and optimize resource allocation.关键词
无人机/春玉米/产量/植被指数/抽雄期/随机森林回归Key words
drone/spring corn/yield/vegetation indices/male extraction period/random forest regression分类
农业科技引用本文复制引用
马世骄,房城泰,赵经华,刘锋,杨庭瑞,袁如芯..基于无人机遥感的春玉米产量预测研究[J].灌溉排水学报,2025,44(1):43-49,65,8.基金项目
国家自然科学基金项目(52169013) （52169013）
新疆维吾尔自治区"十四五"重大专项(2020A01003-4) （2020A01003-4）
自治区研究生科研创新项目(XJ2024G126) （XJ2024G126）
