# A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast

arXiv: https://arxiv.org/abs/1806.09244 · HTML: https://mlsanity.com/paper/1806.09244/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [VITA: Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting](https://mlsanity.com/paper/2508.03589/) (2025)
- [Comparing Machine Learning Techniques for Alfalfa Biomass Yield Prediction](https://mlsanity.com/paper/2210.11226/) (2022)
- [A Comprehensive Modeling Approach for Crop Yield Forecasts using AI-based Methods and Crop Simulation Models](https://mlsanity.com/paper/2306.10121/) (2023)

## Abstract (arXiv)

Yield forecast is essential to agriculture stakeholders and can be obtained with the use of machine learning models and data coming from multiple sources. Most solutions for yield forecast rely on NDVI (Normalized Difference Vegetation Index) data, which is time-consuming to be acquired and processed. To bring scalability for yield forecast, in the present paper we describe a system that incorporates satellite-derived precipitation and soil properties datasets, seasonal climate forecasting data from physical models and other sources to produce a pre-season prediction of soybean/maize yield---with no need of NDVI data. This system provides significantly useful results by the exempting the need for high-resolution remote-sensing data and allowing farmers to prepare for adverse climate influence on the crop cycle. In our studies, we forecast the soybean and maize yields for Brazil and USA, which corresponded to 44% of the world's grain production in 2016. Results show the error metrics for soybean and maize yield forecasts are comparable to similar systems that only provide yield forecast information in the first weeks to months of the crop cycle.