Agriculture has been the world of paramount importance because it feeds the country population alongside contributing to the GDP. Crop yield varies with a mixture of factors including soil properties, climate, and elevation and irrigation technique. Technological developments have fallen short in estimating the yield supported this joint dependence of the said factors. In Indian history, agriculture has been the backbone of the economy. This agricultural activity stay undeveloped because of various factors. Most of the activities stay undeveloped with an absence of recent technology. To create new opportunities and innovation machine learning has emerged with big data technologies and high-performance computing in the agri-technologies domain. Hence, in this paper some machine learning models that learns from Area of Cultivation, Irrigated Land and production to analyze and predict crop yield over seasons in a particular district in the state of Rajasthan has been viewed. For this study, many crops including pulses are considered. The comparative analysis identifies optimal combinations of various machine learning algorithms for selected region to evolve the expectable crop yield/productivity. The algorithms used for predictive analysis gives an idea how a similar dataset can give different outputs using various machine learning algorithm and from which we can confidently choose which algorithm does generate good result in this analysis.
Volume 12 | 03-Special Issue
Pages: 21-26
DOI: 10.5373/JARDCS/V12SP3/20201234