A Survey on Computational Aptitudes towards Precision Agriculture using Data Mining

Abstract

Precision agriculture is a modern agriculture implementation technique in which analysis of numerous source data takes place for decision-making and operation in the management of crop production. The data for precision agriculture are collected through robots, sensors, satellites, and drones. The two approaches of precision agriculture are the predictive approach which is used for representing the static indicator during the crop cycle whereas the control approach is an updating of information Ontology is a demonstration of concepts and their shared association. It can be used in a wide range of contexts, including the classification of agricultural information and the development of knowledge bases. The basic steps involved in precision farming are assessing variation, managing variability, and evaluation. The various tools used in precision farming are the internet of things (IoT), a global positioning system (GPS), geographic information system (GPS), remote sensor, proximate sensor technology, grid sampling, etc. With the increase in information technology in the field of agriculture. Consequently, data mining become much essential for decision-making. This paper attempts to emphasize the coherence of data mining approaches toward helping precision agriculture as a valuable venture.