Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices

Changchun Li, Lei Zhang, Xifang Wu (), Huabin Chai, Hengmao Xiang () and Yinghua Jiao
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Changchun Li: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Lei Zhang: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Xifang Wu: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Huabin Chai: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Hengmao Xiang: Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, China
Yinghua Jiao: Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, China

Agriculture, 2024, vol. 14, issue 11, 1-23

Abstract: A rapid and accurate determination of large-scale winter wheat yield is significant for food security and policy formulation. In this study, meteorological data and enhanced vegetation index (EVI) were used to estimate the winter wheat yield in Henan Province, China, by constructing a deep learning model. The deep learning model combines CNN feature extraction and makes full use of the sequence data processing capability of the LSTM and a multi-head attention mechanism to develop a novel CNN–MALSTM estimation model, which can capture the information of input sequences in different feature subspaces to enhance the expressiveness of the model. A CNN–LSTM baseline model was also constructed for comparison. Compared with the baseline model (R 2 = 0.75, RMSE = 646.53 kg/ha, and MAPE = 8.82%), the proposed CNN–MALSTM model (R 2 = 0.79, RMSE = 576.01 kg/ha, MAPE = 7.29%) could more accurately estimate the yield. Based on the cross-validation with one year of left-out data and the input of the fertility period by fertility period to explore the sensitivity of the model to data from different fertility periods to the final yield, an annual yield distribution map of Henan Province was constructed. Through cross-validation, the stability of the model in different years was assessed. The results showed that the model could obtain the best prediction of the yield approximately 20 days in advance. In terms of the spatial distribution of the yield in Henan Province on a yearly basis, the estimated yield showed an overall uptrend from west to east, consistent with the trend in the statistical yearbook of the yield for Henan Province. Thus, it can be concluded that the proposed CNN–MALSTM model can provide stable yield estimation results.

Keywords: enhanced vegetation index (EVI); meteorological data; long short-term memory networks with multiple attention mechanism (MALSTM); yield estimation; winter wheat (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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