Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data

Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data

Accurate crop yield prediction is vital towards optimizing agricultural productivity. Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. To brid...

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Bibliographic Details
Main Authors: Theodoros Petropoulos, Lefteris Benos, Remigio Berruto, Gabriele Miserendino, Vasso Marinoudi, Patrizia Busato, Chrysostomos Zisis, Dionysis Bochtis
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
precision agriculture
lupin cultivation
spectral reflectance data
vegetation indices
explainable artificial intelligence
SHAP values
Online Access:https://www.mdpi.com/2076-3417/15/13/7074
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