A new article co-authored by Associate Professor Teeradaj Racharak has been published in the journal Artificial Intelligence in Geosciences. The paper, titled “A New Integrated Neurosymbolic Approach for Crop-Yield Prediction Using Environmental Data and Satellite Imagery at Field Scale”, presents a novel neurosymbolic framework that integrates machine learning and symbolic reasoning for more accurate and explainable crop-yield prediction.

The proposed method combines structured environmental data and high-resolution satellite imagery to model crop performance at the field scale. By bridging symbolic knowledge representations with deep learning techniques, this integrated approach enhances both prediction accuracy and interpretability, making it highly relevant for real-world applications in precision agriculture and sustainable land management.

This work reflects an international collaboration and contributes to ongoing research at the Advanced Institute of So-Go-Chi (Convergence Knowledge) Informatics in the area of trustworthy, interdisciplinary AI for societal impact.

Citation:
Khadija Meghraoui, Teeradaj Racharak, Kenza Ait El Kadi, Saloua Bensiali, Imane Sebari, A New Integrated Neurosymbolic Approach for Crop-Yield Prediction Using Environmental Data and Satellite Imagery at Field Scale, Artificial Intelligence in Geosciences, 2025.
DOI: https://doi.org/10.1016/j.aiig.2025.100125