BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation Article Swipe
Lei Zhang
,
Changchun Li
,
Xifang Wu
,
Hengmao Xiang
,
Yinghua Jiao
,
Huabin Chai
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2024.1500499
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2024.1500499
Introduction In 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. Methods Solar-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 Discussion The 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 R ² =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. Conclusion Thus, 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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpls.2024.1500499
- https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1500499/pdf
- OA Status
- gold
- Cited By
- 15
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405654560
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405654560Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fpls.2024.1500499Digital Object Identifier
- Title
-
BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-20Full publication date if available
- Authors
-
Lei Zhang, Changchun Li, Xifang Wu, Hengmao Xiang, Yinghua Jiao, Huabin ChaiList of authors in order
- Landing page
-
https://doi.org/10.3389/fpls.2024.1500499Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1500499/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1500499/pdfDirect OA link when available
- Concepts
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Yield (engineering), Winter wheat, Deep learning, Computer science, Artificial intelligence, Estimation, Remote sensing, Machine learning, Agronomy, Biology, Geography, Engineering, Systems engineering, Materials science, MetallurgyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
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2025: 15Per-year citation counts (last 5 years)
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40Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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