ABSTRACT
Farming is an important occupation in most countries. It is the main supplier of nutrition worldwide. Presently, farming is faced with various obstacles. Of these is the creation of nutrition to cater to a quickly rising population. Moreover, the ecosystem is being damaged along with the atmosphere, which is fluctuating regularly. Consequently, the shift from traditional to advanced farming techniques is inevitable. Precision farming is one of the solutions used to meet the needs of the rapidly growing demand for food. The data plays a very significant role in farming. Farming comprises heterogeneous information about climate, harvests, soil, manure, seed, etc. This data needs to be appropriately handled, and knowledge needs to be mined from it to acquire interpretable results. Data mining (DM) is the process of mining useful information from “raw data”. It is anticipated to perform a vital part in precision farming for performing real-time analysis on big data. Currently, DM is finding its application in several farming domains such as the categorization of properties, forecasting the potency of soil, forecasting harvests, etc. This chapter covers the DM methods presently being utilized in the farming sector. Real-time case studies pertinent to agricultural data mining along with related research areas are also discussed in detail.
