Spatial Data Mining in Agriculture for Estimation of Crop Price

Abstract

Advances in computing and data storage have made it possible to access a tremendous amount of data. The difficulty has been to extract knowledge from this raw data; this has resulted in the development of new methodologies and techniques, such as data processing, that will link information knowledge to agricultural yield estimation. The goal of this study was to evaluate these novel data processing techniques and apply them to the database's various variables to see if any meaningful associations could be discovered. These advance forecasts, however, are merely that: estimates, not target estimates. Many subjective assessments are backed by many qualitative criteria in interpreting these estimates. As a result, there is a demand to establish statistically solid objective crop acreage and production estimates.

The study of geographical data is still in its infancy, and a precise approach for rule mining is required. The technique uses a quick algorithm to mine large data sets in a crude manner, enhancing the standard of mining in a reduced data set. The group to which every one of the excess items is most like is relegated, supported by the distance between the thing and consequently the bunch mean. Each bunch's mean is then recalculated. Emphasize the model capacity until it joins. The previously mentioned idea is utilized in agribusiness, where temperature and precipitation are utilized as the underlying geological information, trailed by agrarian examination.

We will examine the uses and methodologies of information mining in agriculture in this study. A few information handling procedures, like K-Means, K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), are utilized for very current uses of data mining methods. Price prediction has become a significant agricultural problem that can only be solved with the use of accessible data. This challenge will be solved using data processing techniques. Various data processing approaches were tested on various data sets in order to solve this challenge.