Version 1
: Received: 5 September 2024 / Approved: 11 September 2024 / Online: 12 September 2024 (15:50:38 CEST)
How to cite:
Yewle, A. D.; Karakus, O. Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction. Preprints2024, 2024090970. https://doi.org/10.20944/preprints202409.0970.v1
Yewle, A. D.; Karakus, O. Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction. Preprints 2024, 2024090970. https://doi.org/10.20944/preprints202409.0970.v1
Yewle, A. D.; Karakus, O. Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction. Preprints2024, 2024090970. https://doi.org/10.20944/preprints202409.0970.v1
APA Style
Yewle, A. D., & Karakus, O. (2024). Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction. Preprints. https://doi.org/10.20944/preprints202409.0970.v1
Chicago/Turabian Style
Yewle, A. D. and Oktay Karakus. 2024 "Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction" Preprints. https://doi.org/10.20944/preprints202409.0970.v1
Abstract
This research centres on advancing the utilisation of a novel Deep Ensemble model called RicEns-Net for predicting crop yields through sophisticated multi-modal data engineering. Initially, data for the study were acquired from Ernst \& Young (EY), a prominent global technology and management consulting firm, as part of their annual data science challenge (EY Open Science Challenge 2023). The predictive models leverage multi-modal data by integrating radar and optical-type remote sensing data alongside meteorological data to create supplementary features essential for predicting crop yield. The primary data features are derived from spectral indices extracted from Sentinel 2 satellites, while more advanced features are generated through combinations and interactions of these fundamental features. Furthermore, data from Sentinel 1 and 3, along with meteorological measurements such as surface temperature and rainfall, account for the intricate natural factors influencing crop yield, creating a comprehensive set of data features based on existing research. To tackle the complexity introduced by numerous data features, extensive data engineering was conducted to identify the most predictive features among 100+ and mitigate the "curse of dimensionality," resulting in 15 features from 5 different modalities. The proposed RicEns-Net comprises multiple machine learning algorithms in a deep ensemble approach to leverage the best features of each of these techniques, achieving optimal performance. The proposed model achieves an MAE value of 341 kg/Ha for prediction, surpassing the performance of the EY Open Science Challenge 2023 winner models and state-of-the-art deep/machine learning approaches.
Keywords
Crop Yield; Rice Crop; Multi-modal Remote Sensing; Machine Learning; Ensemble Learning; Deep Ensemble Model
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
