Interpretable cotton yield prediction model using Earth Observation time series

Mustafa Serkan Isik, Mehmet Furkan Celik, Esra Erten
July, 2023
Project DOI

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.

Type
Conference paper
Publication
In IEEE International Geoscience and Remote Sensing Symposium 2023
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.

Mustafa Serkan Isik
Mustafa Serkan Isik
PhD in Geosciences

My research interests include water cycle, remote sensing and satellite geodesy, and ML/DL algorithms.