Research Article Open Access

Self-Organizing Map and Multi-Layer Perceptron Neural Network Based Data Mining To Envisage Agriculture Cultivation

E. T. Venkatesh and Dr. P. Thangaraj

Abstract

Study on characteristics of soil, to determine the types of crops suitable for cultivation in a particular region can increase the yield to greater extent, which minimizes the expenditures involved in irrigation and application of fertilizers. With the tested techniques available for calibrating the quality of soil and the crops suitable for cultivation in it, it is possible to determine the exact crop, irrigation patterns and even the cycle and quantity of fertilizer application. This paper dealt with the application of SOM based clustering and Artificial Intelligence techniques, to analyze the patterns of soils distributed across huge geographical area and identify the suitable types of crops for the particular soil. Estimation of exact crop(s) suitable for a particular region can help stave off redundant maintenance and the inherent expenditures that would occur due to over irrigation and over usage of fertilizers, to fulfill the natural deficiencies. Our Focus is to improve the optimal utilization of innate characteristics in a soil through cultivation of appropriate crops, which will increase the volume and quality of yield, in particular for a developing country like India, where the huge majority of the population depends primarily on agriculture for livelihood.

Journal of Computer Science
Volume 4 No. 6, 2008, 494-502

DOI: https://doi.org/10.3844/jcssp.2008.494.502

Submitted On: 28 July 2008 Published On: 30 June 2008

How to Cite: Venkatesh, E. T. & Thangaraj, D. P. (2008). Self-Organizing Map and Multi-Layer Perceptron Neural Network Based Data Mining To Envisage Agriculture Cultivation. Journal of Computer Science, 4(6), 494-502. https://doi.org/10.3844/jcssp.2008.494.502

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Keywords

  • Data mining
  • agriculture
  • soil characteristics
  • clustering
  • unsupervised learning
  • selforganizing map (SOM)
  • multi-layer perceptron neural networks (MLPNN)