The Role of Innovative Data Mining Approaches for Analyzing and Estimating the Crop Yield in Agriculture Among Emerging Nations

Agriculture is the study or practise of cultivating land, including that of the gradual improved soil condition in order to generate harvests for food, fleece, as well as other products. The area of land under cultivation has decreased dramatically over time as a result of expanding urbanisation and industrialisation; also, the agriculture industry has been severely impacted by population reduction and climate change. Data mining is a rapidly new and rapidly growing research-oriented subject in agriculture that is used to formulate and analyse diverse agricultural production situations. In agriculture, data mining might aid in yield prediction, climate and rainfall forecasts, seed and soil conditions, and crop production. In agriculture, predictive data mining is designed to estimate upcoming crop, fertilisers and pesticides that can be used, and income to be made for healthy crop development and function. Different “data mining techniques”, including “k-nearest neighbour (KNN) ”, “support vector machine (SVM)”, and “artificial neural network (ANN)”, are used to analyse different ways and boost agricultural growth. Each analysis technique will have its own unique way of perceiving numerous difficulties and leads to the development of a suitable solution for every agronomic challenge. Aim of this paper is to defining role of “Data Mining Techniques” for estimating crop yield in agricultural context among emerging nations. This research paper has considered mixed method technique (primary quantitative and secondary qualitative) to gather relevant and factual data.