# Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction

arXiv: https://arxiv.org/abs/2502.06062 · HTML: https://mlsanity.com/paper/2502.06062/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 70 references that are in the map.
- First posted on arXiv 9 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Corn Yield Prediction with Ensemble CNN-DNN](https://mlsanity.com/paper/2105.14351/) (2021)
- [Forecasting Corn Yield with Machine Learning Ensembles](https://mlsanity.com/paper/2001.09055/) (2020)
- [Predicting air quality via multimodal AI and satellite imagery](https://mlsanity.com/paper/2211.00780/) (2022)
- [High-precision Density Mapping of Marine Debris and Floating Plastics via Satellite Imagery](https://mlsanity.com/paper/2210.05468/) (2022)
- [Knowledge Distillation for Road Detection based on cross-model Semi-Supervised Learning](https://mlsanity.com/paper/2402.05305/) (2024)

## Built on by (most cited first, 1 of 1)

- [UniCrop: A Universal, Multi-Source Data Engineering Pipeline for Scalable Crop Yield Prediction](https://mlsanity.com/paper/2601.01655/) (2026)

## Abstract (arXiv)

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.