The field of agriculture needs to manage a lot of data and processing and recovery of significant data from this abundance of agricultural information is necessary to support the farmers. In this way, proper strategies and techniques are required for overseeing and sorting out this data to increase the efficiency and agricultural productivity. Data mining can process and convert this crude data into helpful information for improving agriculture. Diseases in fruit cause real production and economic misfortunes just as reduction in both quality and amount of agricultural products By and by a day’s fruit diseases detection has gotten expanding consideration in checking colossal field of harvests. Data mining is the process of discovering and extracting of intriguing patterns and knowledge from a lot of data. In this paper there are different data mining techniques utilized for processing of agricultural information/data such as k-means clustering, k-nearest neighbor, artificial neural networks, support vector machine, naive Bayesian classifier and fuzzy c-means are described.
Keywords: fruit diseases, data mining, k-means clustering, k-nearest neighbour, artificial neural networks, support vector machine, naive Bayesian classifier, fuzzy c-means.