农业机械学报2024，Vol.55Issue(3)：173-182,10.DOI:10.6041/j.issn.1000-1298.2024.03.017
基于遥感多参数和CNN-Transformer的冬小麦单产估测
Yield Estimation of Winter Wheat Based on Multiple Remotely Sensed Parameters and CNN-Transformer
摘要
Abstract
In order to improve the accuracy of winter wheat yield estimation and the phenomena of underestimation of high yield and overestimation of low yield that exist in yield estimation models,the Guanzhong Plain in Shaanxi Province,China was taken as the study area,and the vegetation temperature condition index(VTCI),leaf area index(LAI)and fraction of photosynthetically active radiation(FPAR)at the ten-day interval were selected as remotely sensed parameters,and a deep learning model was proposed for estimating winter wheat yield by combining the local feature extraction capability of convolutional neural network(CNN)and the global information extraction capability of Transformer network based on the mechanism of self-attention.Compared with the Transformer model(R2 was 0.64,RMSE was 465.40 kg/hm2,MAPE was 8.04％),the CNN-Transformer model had higher accuracy in estimating winter wheat yield(R2 was 0.70,RMSE was 420.39 kg/hm2,MAPE was 7.65％),which can extract more yield-related information from the multiple remotely sensed parameters,and improved the underestimation of high yield and overestimation of low yield which existed in the Transformer model.The robustness and generalization ability of the CNN-Transformer model were further validated based on the five-fold cross-validation method and the leave-one-out method.In addition,based on the CNN-Transformer model,the cumulative effect of the winter wheat growth process was revealed,the impact of gradually accumulating ten-day scale input information on yield estimation was analyzed,and the ability of the model to characterize the accumulation process of winter wheat at different growth stages was assessed.The results showed that the model can effectively capture the critical period of winter wheat growth,which was from late March to early May.关键词
冬小麦/作物估产/遥感多参数/卷积神经网络/Transformer模型Key words
winter wheat/yield estimation/multiple remotely sensed parameters/convolutional neural network/Transformer model分类
信息技术与安全科学引用本文复制引用
王鹏新,杜江莉,张悦,刘峻明,李红梅,王春梅..基于遥感多参数和CNN-Transformer的冬小麦单产估测[J].农业机械学报,2024,55(3):173-182,10.基金项目
国家自然科学基金项目(42171332) （42171332）
