Digital Yield Predictions

Yield prediction is a vast area of study involving different fields of science such as agriculture, plant physiology, informatics, and machine learning. Numerous review papers have been published on various aspects of yield prediction. Instead of focusing on certain types of models, data sources, or crops, we provide a general overview of the methods used for forecasting crop yield. We first consider various sources of data used in yield prediction efforts as well as the various measures to assess prediction accuracy. We then give a brief overview on plant physiology-based yield simulation models. Although the main aim of these models is usually not to forecast crop yield as accurately as possible, they describe the phenomena of plant growth that ultimately underlie all efforts related to yield prediction. After that, a more comprehensive overview is given on the various types of machine learning methods applied to yield prediction in exponentially increasing number of studies. We first describe the conventional feature-based machine learning techniques after which the use of several deep learning methods for yield prediction is considered.