A Survey on Agriculture Analysis for Crop Yield Prediction Using Data Mining Techniques
Now a day's agriculture is one of the most important field in the emerging real world and it is the main occupation and backbone of our country. Agriculture is in poor condition since before comparing previous years. The main reasons for this is without a well formed pattern about farming and proper guidance to the farmers. Due to these problems, farming affects the yield of crop and unawareness about the crop cultivation methodologies. And also season to cultivate the crop and choosing which soil is the best to cultivate the particular crop based on the weather condition and also when to harvest the crop for the best yield. If the farmer is aware about the crop cultivation methodologies and harvesting it will more helpful for the people in the real world and also to maximize the crop productivity. Data mining is the process of finding new template from large data sets, this technology which is employed in inferring useful knowledge that can be put to use from a vast amount of data, various data mining techniques such as classification, prediction, clustering and outlier analysis can be used for the purpose. Climate is one of the meteorological data that is rich by important knowledge. This paper presents a brief comparative study of various papers that deal with various techniques used to figure out the crop yield. Different data mining techniques that are in use for the crop yield estimation are K Means, K Nearest neighbor (KNN) Artificial Neural Networks(ANN) Recently data processing techniques in agriculture required decide, storage, monitored and retrieval the resources used. This survey aims to search out appropriate data processing models to realize high accuracy and prediction capabilities.