Performance Evaluation of Machine Learning Algorithms for a Cluster-based Crop Recommendation System.

A cluster-based crop recommendation system categorizes the crop candidates into several groups or classes (e.g., based on soil and environment parameters similarities). After receiving a request from a farmer, it recommends the most appropriate group of crops to the farmer. Proposing a group of crops (i.e., more than one crop) can allow farmers to consider their personal interests as well. In addition, the cluster-based crop recommendation system can reduce the complexity and utilized resources (e.g., compute resource). As the main contribution of this paper, we evaluate the performance of different Machine Learning (ML) algorithms to find the most appropriate one for using in the cluster-based crop recommendation system.