Yield Prediction Model based on Multitemporal Satellite Data and Open Public Data: Case Study for Territory of Bulgaria

The monitoring of vegetation dates back to the 1970s, but ever since, it has also been a very crucial task for providing a good quality and quantity of food supplies. In the following article, the authors present their technological scheme and results from processing a prediction yield model for agricultural fields in Bulgaria. The team used open public data in a vector format presenting all crop fields subsided by the state and machine learning techniques for satellite image classification for the territory of the whole country. The authors decided to use optical data from Sentinel-2. Multitemporal data were used in order to train a prediction model to help farmers predict their crop yield. The authors also focus on the struggles using big data for the whole country and any ambiguities throughout the computation process. The basic technological scheme is as follows: data preparation (vector, meta, and imagery data), defining a proper coordinate system for the whole country, machine learning application using the defined region of interest and a segmentation network, and classification using 25 predefined classes. The results of this paper aim to present a reliable technological method for both farmers and the state to monitor the current state of certain crops and to predict with high accuracy any future yield.