Interpretable Cotton Yield Prediction Model Using Earth Observation Time Series

Mustafa Serkan Isik, Mehmet Furkan Celik, Esra Erten*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

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 languageEnglish
Title of host publicationIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3442-3445
Number of pages4
ISBN (Electronic)9798350320107
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States
Duration: 16 Jul 202321 Jul 2023

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2023-July

Conference

Conference2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Country/TerritoryUnited States
CityPasadena
Period16/07/2321/07/23

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.

FundersFunder number
Climate Change AI

    Keywords

    • Cotton
    • Crop yield
    • Explainable Artificial Intelligent
    • Long Short-Term Memory (LSTM)
    • Predictive models
    • SHAP
    • Shapley values
    • XAI

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