An Advanced Methodology for Smart Agriculture Forecasting Agricultural Commodity Price and Yield Using Time Series Data Analytic Technique

Abstract

The agriculture sector in India contributes more than 15 percent for our Gross Domestic newlineproduct (GDP) in 2022. The India is basically agriculturally based country where more than newlinesixty percent of the population depends on agriculture. The progress in terms of advancement newlinein technology is very poor in agricultural sector which trails the sector in all the dimensions. newlineThe whole worlds population is increasing exponentially it leads the need of agriculture and newlinefood safety [1]. The current traditional method of farming will not meet the requirement of newlineworld s food desires. In current scenario there is some need of implementing current emerging newlinetechnologies in agricultural sector that lead to transforms traditional agriculture to smart newlineagriculture. Data analytics plays an important role in agriculture sector for incorporating newlinerecommendation system and prediction system [2]. The use of technology and network in newlineagriculture will give one more label name as IT agriculture or smart agriculture. The smart newlineagriculture system is a state-of-the-art technology comprised of sensor knowledge, automation newlinecontrol, digital network transmission, information storage, and information processing to newlineprovide effective solutions for a target range of farm applications [3]. There are many newlinetechnologies that helps to achieve smart agricultural system in which data analytics plays a newlinesignificant role is discussed in the paper. The price prediction system needs price dataset of newlinemore than ten years. Based on the nature of the dataset the time series data analytic approach newlineis suitable and recommended. The time series data analytics technique known as Auto newlineRegressive Integrated Moving Average (ARIMA) model is used to predict the price of arecanut newlinein Puttur Taluk of Dakshin Kannada district of Karnataka State. ARIMA model is very suitable newlinemodel to process time series dataset [4]. This paper demonstrates how ARIMA model newlinedeveloped for price dataset. The research uses data analysis tools and packages to attain and newlinecompare the res