Crop Yield Prediction: An Operational Approach to Crop Yield Modeling on Field and Subfield Level with Machine Learning Models

Accurate and reliable crop yield prediction is a complex task. The yield of a crop depends on a variety of factors whose accurate measurement and modeling is challenging. At the same time, reliable yield prediction is highly desirable for farmers to optimize crop production. In this paper, we introduce a modeling based on remote sensing data and Machine Learning models evaluated on a large-scale dataset to address the challenge of an operational crop yield estimation and forecasting on field and subfield level. With our approach, we aim towards a global yield modeling based on Machine Learning models which operates across crop types without the need for crop-specific modeling. We demonstrate that our approach learns to map in-field variability for all studied crop types. Overall, the predictions have an error (RRMSE) of around 15% and an R² value of 0.77 at field level.

1. INTRODUCTION

With climate conditions changing at an accelerated pace, reliable crop yield modeling is becoming increasingly vital. However, predicting crop yields accurately and reliably is a complex task. The yield of a crop depends on a variety of factors, and accurately measuring and modeling them is challenging. Factors such as soil conditions, weather, management practices, but also environmental conditions affect the yield of a crop considerably. At the same time, a model capable of reliably predicting yield can support farmers in essential crop management decisions toward sustainable crop production while leveraging yield potential, which is particularly important with globally changing climate conditions [1].