Informative Earth Observation Variables for Cotton Yield Prediction Using Explainable Boosting Machine


Çelik M. F., Serkan Isik M., Erten E., Taskin G.

2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, California, United States Of America, 16 - 21 July 2023, vol.2023-July, pp.3542-3545 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2023-July
  • Doi Number: 10.1109/igarss52108.2023.10282371
  • City: California
  • Country: United States Of America
  • Page Numbers: pp.3542-3545
  • Keywords: cotton, crop yield, Explainable Artificial Intelligence, Explainable Boosting Machine, feature importance
  • Istanbul Technical University Affiliated: Yes

Abstract

Cotton, a vital crop in the global textile industry, faces challenges from climate and ecosystem changes. Accurate cotton yield prediction is crucial for the economy and environmental sustainability, and it requires a deep understanding of the complex relationship between its parameters and yield. To achieve this, a comprehensive approach integrating climatic factors, soil parameters, and biophysical parameters observed through high-resolution remote sensing satellites was employed. This study utilized a multisource dataset to develop a predictive model for cotton yield over Turkiye, allowing accurate yield estimation and understanding the impact of the Earth Observation (EO)-based yield predictors on the model. Specifically, we utilized the Explainable Boosting Machine (EBM) algorithm to model and predict cotton yield while offering insights into selecting EO predictors. Additionally, we conducted a performance evaluation of our proposed approach in comparison to popular boosting-based algorithms like eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and Light gradient boosting (Light-GBM).