Estimating crop yields with remote sensing and deep learning

TL;DR

This paper introduces a deep learning model that leverages crop calendars, remote sensing, and weather data to improve crop yield predictions before and during the growing season, addressing issues with traditional NDVI-based methods.

Contribution

A novel deep learning approach that combines crop calendars, remote sensing, and weather forecasts for more accurate crop yield estimation.

Findings

Effective pre-season and in-season yield predictions for five crops.

Overcomes limitations of NDVI data affected by clouds and shadows.

Provides a practical tool for farmers and insurers.

Abstract

Increasing the accuracy of crop yield estimates may allow improvements in the whole crop production chain, allowing farmers to better plan for harvest, and for insurers to better understand risks of production, to name a few advantages. To perform their predictions, most current machine learning models use NDVI data, which can be hard to use, due to the presence of clouds and their shadows in acquired images, and due to the absence of reliable crop masks for large areas, especially in developing countries. In this paper, we present a deep learning model able to perform pre-season and in-season predictions for five different crops. Our model uses crop calendars, easy-to-obtain remote sensing data and weather forecast information to provide accurate yield estimates.