Abstract: Agriculture is the field of interest in today's technology emerging world. It is the main occupation and backbone of our country. As India’s population currently stands at 1.3 billion people and is projected to grow eight times of current population by 2024, its become a critical challenge for the farmers to feed the population. And also the various environmental changes in the developing world are posing an important threat to the agricultural economy. Hence food security enhancement requires the transition to agricultural production systems that are more productive. The need to incorporate Information technologies into the task of food production is very important. Crop yield prediction is one of the important factors that provide information for decision makers to maximize the crop productivity but it is a problem that needs to be solved based on available data. Data mining technology serves to be a better choice for this purpose and has become an interesting and recent research topic in agriculture to predict the crop yield. This paper presents a brief comparative study of various papers that deal with various techniques used to predict the crop yield. From the data that is readily available, the data mining techniques give a complete picture about the estimation of crop yield. Different data mining techniques that are in use for the crop yield estimation are K-Means, K-Nearest neighbor (KNN).
Keywords: K-Means, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multiple Linear Regression (MLR).