Developing an Agricultural Product Price Prediction Model using HADT Algorithm

Abstract

Big Data Predictive Analytics and Data mining are emerging recent research field to analyse the agricultural crop price. The applications and techniques of data mining as well as Big Data using agriculture data is considered in this paper. In particular, the farmers are more concern about estimating that how much profit they are about to expect for the chosen crop. As with many other sectors the amount of agriculture data are increasing on a daily source. In this work, agriculture crop price dataset of Virudhunagar District, Tamilnadu, India is considered and for the price prediction model based on data mining decision tree techniques. The main goal is to establish the new predictive model based on Hybrid Association rule-based Decision Tree algorithm (HADT). The outcome for the suggested HADT forecast model is heartening and precise to predict agricultural product prices than other current decision tree models.

Prediction of arabica coffee production using artificial neural network and multiple linear regression techniques

“…Forecasting applications have recently been used to predict demand, supply management, and resources to reduce mistakes in terms of overproduction and operating costs 15,16 . Prediction in agriculture is mainly made in terms of rainfall estimates, area, production or productivity, and price 17,18 . Thus, these factors should be used to predict the productivity and yield of agricultural products, possibly with a machine learning model, as shown in Fig.…”