Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction
This research addresses the problem of accurate crop yield prediction for farmers, policymakers, and other stakeholders, which is crucial for food security and sustainable agriculture, and presents an incremental improvement over existing models.
This study tackled the problem of crop yield prediction by introducing a novel deep ensemble model called RicEns-Net, which achieved a mean absolute error (MAE) of 341 kg/Ha, corresponding to 5-6% of the lowest average yield in the region. The model significantly outperformed previous state-of-the-art models.
This study introduces RicEns-Net, a novel Deep Ensemble model designed to predict crop yields by integrating diverse data sources through multimodal data fusion techniques. The research focuses specifically on the use of synthetic aperture radar (SAR), optical remote sensing data from Sentinel 1, 2, and 3 satellites, and meteorological measurements such as surface temperature and rainfall. The initial field data for the study were acquired through Ernst & Young's (EY) Open Science Challenge 2023. The primary objective is to enhance the precision of crop yield prediction by developing a machine-learning framework capable of handling complex environmental data. A comprehensive data engineering process was employed to select the most informative features from over 100 potential predictors, reducing the set to 15 features from 5 distinct modalities. This step mitigates the ``curse of dimensionality" and enhances model performance. The RicEns-Net architecture combines multiple machine learning algorithms in a deep ensemble framework, integrating the strengths of each technique to improve predictive accuracy. Experimental results demonstrate that RicEns-Net achieves a mean absolute error (MAE) of 341 kg/Ha (roughly corresponds to 5-6\% of the lowest average yield in the region), significantly exceeding the performance of previous state-of-the-art models, including those developed during the EY challenge.