Estimating Soybean Yields Using Causal Inference and Deep Learning Approaches With Satellite Remote Sensing Data
Timely and accurate crop yield estimation is crucial for managing crops, trade, and food security. The combination of remote sensing technology with machine learning methods is increasingly popular for global yield prediction. However, traditional machine learning methods rely on data correlation ra...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10614767/ |
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| author | Fumin Wang Jiale Li Dailiang Peng Qiuxiang Yi Xiaoyang Zhang Jueyi Zheng Siting Chen |
| author_facet | Fumin Wang Jiale Li Dailiang Peng Qiuxiang Yi Xiaoyang Zhang Jueyi Zheng Siting Chen |
| author_sort | Fumin Wang |
| collection | DOAJ |
| description | Timely and accurate crop yield estimation is crucial for managing crops, trade, and food security. The combination of remote sensing technology with machine learning methods is increasingly popular for global yield prediction. However, traditional machine learning methods rely on data correlation rather than causality, leading to poor interpretability. To address this issue, we propose a novel approach that combines a structural causal model (SCM) with deep learning to develop a causal graph attention network model (SCM-GAT) based on causal relationships for soybean yield prediction at the county level of 10 major soybean-producing states in the United States. The SCM-GAT model considers not only conventional vegetation indices and weather variables but also the causal relationships between variables as inputs. Using independent validation and five-fold cross-validation strategies, our results show that the predictive performance of the SCM-GAT model is not only superior to traditional prediction models, such as selection operator regression (LASSO), random forest, but also superior to the common deep learning models based on correlation relationships, such as long short-term memory network, and transformer. It has also proved to be more robust than other models under extreme weather events. In addition, we identify branching to blooming and blooming to setting pods as the key growth phases for soybean yield, and adding these phases as inputs to the model further improves prediction accuracy. Our findings suggest that incorporating causal relationships in deep learning models can improve prediction accuracy. This study provides a novel approach for remote sensing prediction of crop yield. |
| format | Article |
| id | doaj-art-74875ffdded24fa882e4d4078dbe363f |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-74875ffdded24fa882e4d4078dbe363f2024-12-18T00:00:18ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117141611417810.1109/JSTARS.2024.343569910614767Estimating Soybean Yields Using Causal Inference and Deep Learning Approaches With Satellite Remote Sensing DataFumin Wang0https://orcid.org/0000-0002-5078-358XJiale Li1https://orcid.org/0000-0003-0754-1793Dailiang Peng2https://orcid.org/0009-0006-5101-2935Qiuxiang Yi3Xiaoyang Zhang4Jueyi Zheng5Siting Chen6Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou, ChinaInstitute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaSchool of Geomatics and Municipal Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou, ChinaDepartment of Geography & Geospatial Sciences, South Dakota State University, Brookings, SD, USAInstitute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou, ChinaInstitute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou, ChinaTimely and accurate crop yield estimation is crucial for managing crops, trade, and food security. The combination of remote sensing technology with machine learning methods is increasingly popular for global yield prediction. However, traditional machine learning methods rely on data correlation rather than causality, leading to poor interpretability. To address this issue, we propose a novel approach that combines a structural causal model (SCM) with deep learning to develop a causal graph attention network model (SCM-GAT) based on causal relationships for soybean yield prediction at the county level of 10 major soybean-producing states in the United States. The SCM-GAT model considers not only conventional vegetation indices and weather variables but also the causal relationships between variables as inputs. Using independent validation and five-fold cross-validation strategies, our results show that the predictive performance of the SCM-GAT model is not only superior to traditional prediction models, such as selection operator regression (LASSO), random forest, but also superior to the common deep learning models based on correlation relationships, such as long short-term memory network, and transformer. It has also proved to be more robust than other models under extreme weather events. In addition, we identify branching to blooming and blooming to setting pods as the key growth phases for soybean yield, and adding these phases as inputs to the model further improves prediction accuracy. Our findings suggest that incorporating causal relationships in deep learning models can improve prediction accuracy. This study provides a novel approach for remote sensing prediction of crop yield.https://ieeexplore.ieee.org/document/10614767/Causal inferencedeep learninggraph attention network (GAT)soybean yield predictionstructural causal model (SCM) |
| spellingShingle | Fumin Wang Jiale Li Dailiang Peng Qiuxiang Yi Xiaoyang Zhang Jueyi Zheng Siting Chen Estimating Soybean Yields Using Causal Inference and Deep Learning Approaches With Satellite Remote Sensing Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Causal inference deep learning graph attention network (GAT) soybean yield prediction structural causal model (SCM) |
| title | Estimating Soybean Yields Using Causal Inference and Deep Learning Approaches With Satellite Remote Sensing Data |
| title_full | Estimating Soybean Yields Using Causal Inference and Deep Learning Approaches With Satellite Remote Sensing Data |
| title_fullStr | Estimating Soybean Yields Using Causal Inference and Deep Learning Approaches With Satellite Remote Sensing Data |
| title_full_unstemmed | Estimating Soybean Yields Using Causal Inference and Deep Learning Approaches With Satellite Remote Sensing Data |
| title_short | Estimating Soybean Yields Using Causal Inference and Deep Learning Approaches With Satellite Remote Sensing Data |
| title_sort | estimating soybean yields using causal inference and deep learning approaches with satellite remote sensing data |
| topic | Causal inference deep learning graph attention network (GAT) soybean yield prediction structural causal model (SCM) |
| url | https://ieeexplore.ieee.org/document/10614767/ |
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