Estimating crop yields with remote sensing and deep learning
This work addresses crop yield estimation for farmers and insurers, but it appears incremental as it builds on existing remote sensing and deep learning approaches without a major breakthrough.
The paper tackles the problem of inaccurate crop yield predictions by developing a deep learning model that uses crop calendars, remote sensing data, and weather forecasts to provide pre-season and in-season estimates for five crops.
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.