An Efficient Method for Soil Analysis using FARM-IT Approach
Melvin Martis, Paul Lobo and Shreesh Chavan. Preventing SQLIA using ORM Tool with HQL. International Journal of Applied Information Systems 11(6):16-19, November 2016. URL, DOI BibTeX
@article{10.5120/ijais2016451618, author = "Melvin Martis and Paul Lobo and Shreesh Chavan", title = "Preventing SQLIA using ORM Tool with HQL", journal = "International Journal of Applied Information Systems", issue_date = "November 2016", volume = 11, number = 6, month = "Nov", year = 2016, issn = "2249-0868", pages = "16-19", numpages = 4, url = "http://www.ijais.org/archives/volume11/number6/947-2016451618", doi = "10.5120/ijais2016451618", publisher = "Foundation of Computer Science (FCS), NY, USA", address = "New York, USA" }
Abstract
Agriculture has benefited from improvement in data mining and automation, in recent times. Based on training dataset, this paper suggests a data mining method to analyze soil content such as pH, Field Capacity, Wilting Point, Nitrogen, Potassium, Phosphorus, etc. and helps determine the amount of fertilizer required. Additionally, an irrigation facility based on automated detection of moisture can help optimize the use of water. This system uses a server. The sensors output to server through a distributed network connected wirelessly via a tool such as, ZigBee. This project aims at optimizing the use of fertilizer and water, and accordingly, suggest proper actions.
Reference
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Keywords
Naïve Bayes, Iterative Dichotomiser 3 (ID3), WEKA tool, Data mining, NPK values, Decision tree, Algorithms