Viable crop prediction scenario in bigdata using a novel approach

Abstract

Agriculture leads a vital role in the human surveillance where it lies as the initial step for the human civilization. Due to the excessive need for food, the agricultural practices are in large-scale production as a business which is termed as “Agribusiness”. Modern agriculture allows both biological and technological developments such as plant breeding, agrochemicals, genetic breeding, remote sensing, crop monitoring, sensor nodes, and automatic maintenance system. The integration of sensor nodes in the farming field leads to the generation of huge data which can need a learning algorithm for analyses to determine a specific solution. There are various machine learning algorithms in practices which are not suitable for handling large datasets. In this paper, a novel algorithm “Agrifi-prediction algorithm” is created which has the functionality of loading the dataset in hdfs and comparing the previous dataset with the current processing dataset. The experimental process is carried out by using the Hadoop framework with MapReduce programming model by analyzing the meteorological and soil dataset and finally compared with the machine learning algorithm to evaluate the accuracy.