D2.1 Report on state-of-the-art algorithms

This article presents a selective survey on articles for Data Mining in the field of Agriculture. The main purpose f the article is to research what algorithms are the most used in this field for different aspects like temperature, humidity, but also predictions like crop yield and crop health. The research has revealed that certain algorithms, like Support Vector Machines and k-Nearest Neighbour tend to offer better results, so they are favored. Also, in production, ensemble algorithms may offer advantages over using single algorithms. Future research could target distributed learning and other methodologies that improve data safety and privacy, and also improve the cost-effectiveness of Data Discovery Platforms.