Comparative Analysis of Data Mining Techniques to Enhance the Decision Making in Crop Yield.

Abstract

Data mining software has been developed by both commercial and academic organisations, and it employs a range of methodologies. These strategies have been used by a variety of organisations, including industrial, commercial, and academic institutions. Massive data sets can be analysed to discover useful categories and trends, for example, through the use of data mining. The use of a variety of data mining technologies to analyse these agricultural data sets is discussed in detail below. The aim of this study is to develop an early prediction strategy for farmers' cost-benefit analyses. This study suggests a number of models and computational approaches in order to prevent agricultural communities from incurring losses or acquiring debt as a result of their efforts. It is possible to make more effective decisions on farm management and agribusiness activities with the assistance of Agriculture Intelligence, such as determining the best cultivars to plant on their farm, determining the optimal cultivation date, Investment Prioritizing, and Evaluate demand and supply risk, with the assistance of Agriculture Intelligence. After that, you must decide on the level of precision.