Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices
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...
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2024-11-01
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| author | Changchun Li Lei Zhang Xifang Wu Huabin Chai Hengmao Xiang Yinghua Jiao |
| author_facet | Changchun Li Lei Zhang Xifang Wu Huabin Chai Hengmao Xiang Yinghua Jiao |
| author_sort | Changchun Li |
| collection | DOAJ |
| description | 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<sup>2</sup> = 0.75, RMSE = 646.53 kg/ha, and MAPE = 8.82%), the proposed CNN–MALSTM model (R<sup>2</sup> = 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. |
| format | Article |
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| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-7b3d299ee00e4d30a1fee200209c96fd2024-11-26T17:43:31ZengMDPI AGAgriculture2077-04722024-11-011411196110.3390/agriculture14111961Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing IndicesChangchun Li0Lei Zhang1Xifang Wu2Huabin Chai3Hengmao Xiang4Yinghua Jiao5School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaShandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, ChinaShandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, ChinaA 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<sup>2</sup> = 0.75, RMSE = 646.53 kg/ha, and MAPE = 8.82%), the proposed CNN–MALSTM model (R<sup>2</sup> = 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.https://www.mdpi.com/2077-0472/14/11/1961enhanced vegetation index (EVI)meteorological datalong short-term memory networks with multiple attention mechanism (MALSTM)yield estimationwinter wheat |
| spellingShingle | Changchun Li Lei Zhang Xifang Wu Huabin Chai Hengmao Xiang Yinghua Jiao Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices Agriculture enhanced vegetation index (EVI) meteorological data long short-term memory networks with multiple attention mechanism (MALSTM) yield estimation winter wheat |
| title | Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices |
| title_full | Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices |
| title_fullStr | Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices |
| title_full_unstemmed | Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices |
| title_short | Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices |
| title_sort | winter wheat yield estimation by fusing cnn malstm deep learning with remote sensing indices |
| topic | enhanced vegetation index (EVI) meteorological data long short-term memory networks with multiple attention mechanism (MALSTM) yield estimation winter wheat |
| url | https://www.mdpi.com/2077-0472/14/11/1961 |
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