Abstract
This study aimed to assess the influence of Earth observation (EO) time series data, specifically soil properties, climate variables, and Enhanced Vegetation Index, on predicting cotton yield using an explainable artificial intelligence model. By utilizing statistical yield data acquired at the commune level in Turkey between 2019-2021, we developed a model for predicting cotton yield. The model employed the Long Short-Term Memory (LSTM) architecture and incorporated the SHapley Additive exPlanations (SHAP) method as a post-hoc method to explain how EO features impact the cotton yield and to interpret the relationship between these features and the variations in yield data.
Original language | English |
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Title of host publication | IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3442-3445 |
Number of pages | 4 |
ISBN (Electronic) | 9798350320107 |
DOIs | |
Publication status | Published - 2023 |
Event | 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States Duration: 16 Jul 2023 → 21 Jul 2023 |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Volume | 2023-July |
Conference
Conference | 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 |
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Country/Territory | United States |
City | Pasadena |
Period | 16/07/23 → 21/07/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Funding
∗This project entitled ”Improving Resiliency of Malian Farmers with Yield Estimation: IMPRESSYIELD” was funded by the Climate Change AI Innovation Grants program, hosted by Climate Change AI with the additional support of Canada Hub of Future Earth.
Funders | Funder number |
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Climate Change AI |
Keywords
- Cotton
- Crop yield
- Explainable Artificial Intelligent
- Long Short-Term Memory (LSTM)
- Predictive models
- SHAP
- Shapley values
- XAI