# Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level

arXiv: https://arxiv.org/abs/2308.08948 · HTML: https://mlsanity.com/paper/2308.08948/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 15 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Built on by (most cited first, 7 of 7)

- [Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction](https://mlsanity.com/paper/2401.11844/) (2024)
- [Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation](https://mlsanity.com/paper/2407.15512/) (2024)
- [An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks](https://mlsanity.com/paper/2504.06915/) (2025)
- [In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data](https://mlsanity.com/paper/2403.16582/) (2024)
- [Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction](https://mlsanity.com/paper/2510.18648/) (2025)
- [YieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction](https://mlsanity.com/paper/2604.00940/) (2026)
- [Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing](https://mlsanity.com/paper/2407.08274/) (2024)

## Abstract (arXiv)

We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use high-resolution crop yield maps as ground truth data to train crop and machine learning model agnostic methods at the sub-field level. We use Sentinel-2 satellite imagery as the primary modality for input data with other complementary modalities, including weather, soil, and DEM data. The proposed method uses input modalities available with global coverage, making the framework globally scalable. We explicitly highlight the importance of input modalities for crop yield prediction and emphasize that the best-performing combination of input modalities depends on region, crop, and chosen model.