Comprehensive Survey on Intelligent Prognostication of Profitable Farming
Keywords:
Agriculture, crop prediction, machine learning, feature selection, redundancy, classification, fertilizers, IndustriesAbstract
Nowadays, agriculture has become an important field of research. Particularly, when it comes to crop prediction, agriculture depends on soil, climate, and temperature. Formerly, farmers used to decide which crop to cultivate, what is needed for its growth, and when to harvest it at its best based on their expertise. But due to continuous changes in environmental conditions, it has become very difficult to take any decision related to farming manually. Choosing the right crop can lead to more yield and more profit for farmer. Therefore, we have begun to use machine learning techniques for crop prediction in recent years. As crop prediction depends on various environmental factors, deciding these factors is also a crucial part of feature selection. To guarantee that the machine learning model that we are using is working at its best and accurately, we need to use correct feature selection process which can convert raw data into precise dataset with minimum redundancy and more significant features for our model. In this study, we are looking for various feature selection as well as classification methods to be used in suggesting suitable crop mainly in Sangli, Satara, and Kolhapur region in Maharashtra. Along with crop and fertilizers to increase yield, we also propose surrounding industries where farmers can sell the yield and maximize their profit.
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