Interpretable Cotton Yield Prediction Model Using Earth Observation Time Series

Işık M. S., Çelik M. F., Erten E.

2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, California, United States Of America, 16 - 21 July 2023, vol.2023-July, pp.3442-3445 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2023-July
  • Doi Number: 10.1109/igarss52108.2023.10281702
  • City: California
  • Country: United States Of America
  • Page Numbers: pp.3442-3445
  • Keywords: Cotton, Crop yield, Explainable Artificial Intelligent, Long Short-Term Memory (LSTM), Predictive models, SHAP, Shapley values, XAI
  • Istanbul Technical University Affiliated: Yes


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.