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

TL;DR

This paper presents a scalable pre-season crop yield forecasting system that uses satellite-derived climate and soil data, eliminating the need for time-consuming NDVI data, and achieves comparable accuracy to existing methods.

Contribution

The system integrates multiple data sources for pre-season yield prediction without relying on NDVI, enhancing scalability and early forecasting capabilities.

Findings

Forecast accuracy for soybean and maize yields is comparable to existing systems.

The system covers Brazil and USA, representing 44% of global grain production in 2016.

Eliminates the need for high-resolution remote sensing data.

Abstract

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.