Classification of Agriculture Land Equipment Using Data Mining Techniques

  • Sakthipriya Dhinakaran, Chandrakumar Thangavel

Abstract

As agriculture meets digital technologies, a new frontier of innovation is emerging and creating multiple pathways to a smart farming future. This paper presents a case study of a smart farming innovation originating from a small-to-medium sized enterprise (SME) that designs and manufactures machinery used in broad-acre, conservation tillage farming. Increasing the efficiency of agriculture production is a very important task. To solve this problem, it is proposed to use the “Smart Farming”. The paper covers verification, and visualization of knowledge representation about the agricultural machinery, equipment and other material resources, as well as peculiarities of the tasks of precision farming. The knowledge base of plant production, built on ontological principles, will be useful to enterprise managers, agronomists, machine operators, planning services and other specialists of large, medium and small farms, as well as to individual farmers. This research aimed to assess these new data mining techniques and apply them to an Agricultural Machinery database to establish if meaningful relationships can be found. A large data set of Machinery database is extracted from the Agricultural College in Madurai. The database contains measurements of Agriculture equipment profile data from various locations of Ramnad, Theni, Madurai, Viruthunagar, Dindigul District. The research establishes whether Equipment are Classified Using various data mining techniques. In addition, comparison was made between Naive bayes classification and Random tree analyses the most effective technique. The outcome of the research may have many benefits, to agriculture machinery implemented in smart farming environmental. Compared the existing feature selection methods, the proposed experimental results shows that the Navie bayes classification algorithm is compared Random Forest algorithm for agricultural data analysis produce a high accuracy and less processing time.

Published
2021-11-24
How to Cite
Sakthipriya Dhinakaran, Chandrakumar Thangavel. (2021). Classification of Agriculture Land Equipment Using Data Mining Techniques. Design Engineering, 15978-15987. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/6749
Section
Articles