BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation
IntroductionIn the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employe...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1500499/full |
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| author | Lei Zhang Changchun Li Xifang Wu Hengmao Xiang Yinghua Jiao Huabin Chai |
| author_facet | Lei Zhang Changchun Li Xifang Wu Hengmao Xiang Yinghua Jiao Huabin Chai |
| author_sort | Lei Zhang |
| collection | DOAJ |
| description | IntroductionIn the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation.MethodsSolar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring. However, its effectiveness for yield estimation under various data fusion conditions has not been thoroughly explored. This study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM). The Bayesian Optimization (BOM) method was employed to determine the optimal hyperparameters for model parameter optimization. Traditional remote sensing variables (TS), such as the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI), were fused with the SIF and climate data to estimate the winter wheat yields in Henan Province, exploring the SIF’s estimation capabilities using various datasets.Results and DiscussionThe results demonstrated that the BCBL model, integrating TS, climate, and SIF data, outperformed other models (e.g., LSTM, Transformer, RF, and XGBoost) in the estimation accuracy, with R²=0.81, RMSE=616.99 kg/ha, and MRE=7.14%. Stepwise sensitivity analysis revealed that the BCBL model reliably identified the critical stage of winter wheat yield formation (early March to early May) and achieved high yield estimation accuracy approximately 25 d before harvest. Furthermore, the BCBL model exhibited strong stability and generalization across different climatic conditions.ConclusionThus, the BCBL model combined with SIF data can offer reliable winter wheat yield estimates, hold significant potential for application, and provide valuable insights for agricultural policymaking and field management. |
| format | Article |
| id | doaj-art-a14050ba9ebb4904a59b4e112f39f63a |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-a14050ba9ebb4904a59b4e112f39f63a2024-12-20T05:10:09ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-12-011510.3389/fpls.2024.15004991500499BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimationLei Zhang0Changchun Li1Xifang Wu2Hengmao Xiang3Yinghua Jiao4Huabin Chai5School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaShandong Provincial Land Survey and Planning Institute, Jinan, Shandong, ChinaShandong Provincial Land Survey and Planning Institute, Jinan, Shandong, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaIntroductionIn the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation.MethodsSolar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring. However, its effectiveness for yield estimation under various data fusion conditions has not been thoroughly explored. This study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM). The Bayesian Optimization (BOM) method was employed to determine the optimal hyperparameters for model parameter optimization. Traditional remote sensing variables (TS), such as the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI), were fused with the SIF and climate data to estimate the winter wheat yields in Henan Province, exploring the SIF’s estimation capabilities using various datasets.Results and DiscussionThe results demonstrated that the BCBL model, integrating TS, climate, and SIF data, outperformed other models (e.g., LSTM, Transformer, RF, and XGBoost) in the estimation accuracy, with R²=0.81, RMSE=616.99 kg/ha, and MRE=7.14%. Stepwise sensitivity analysis revealed that the BCBL model reliably identified the critical stage of winter wheat yield formation (early March to early May) and achieved high yield estimation accuracy approximately 25 d before harvest. Furthermore, the BCBL model exhibited strong stability and generalization across different climatic conditions.ConclusionThus, the BCBL model combined with SIF data can offer reliable winter wheat yield estimates, hold significant potential for application, and provide valuable insights for agricultural policymaking and field management.https://www.frontiersin.org/articles/10.3389/fpls.2024.1500499/fullbidirectional long short-term memory (BiLSTM)1D convolutional neural network (1D CNN)Bayesian optimization (BO)solar-induced chlorophyll fluorescence (SIF)yield estimation |
| spellingShingle | Lei Zhang Changchun Li Xifang Wu Hengmao Xiang Yinghua Jiao Huabin Chai BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation Frontiers in Plant Science bidirectional long short-term memory (BiLSTM) 1D convolutional neural network (1D CNN) Bayesian optimization (BO) solar-induced chlorophyll fluorescence (SIF) yield estimation |
| title | BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation |
| title_full | BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation |
| title_fullStr | BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation |
| title_full_unstemmed | BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation |
| title_short | BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation |
| title_sort | bo cnn bilstm deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation |
| topic | bidirectional long short-term memory (BiLSTM) 1D convolutional neural network (1D CNN) Bayesian optimization (BO) solar-induced chlorophyll fluorescence (SIF) yield estimation |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1500499/full |
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