A Survey on Data Mining and Pattern Recognition Techniques for Soil Data Mining

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                                                         This research work aims to compare the performance of the data mining algorithms with soil limitations and soil conditions in respect of the following characteristics: Acidity, Alkalinity and sodality, Salinity, Phosphorus fixation, cracking and swelling properties, Depth, Soil density and Nutrient content. This overall process of finding useful knowledge in raw data involves the following steps:

1.     Developing an understanding of the application domain
2.     Creating a target dataset based on an intelligent way of selecting data by focusing on a subset of variables or data samples
3.     Data cleaning and pre–processing
4.     Data reduction and projection
5.     Choosing the data mining task
6.     Choosing the data mining algorithm
7.     The data mining process
8.      Interpreting mined patterns with possible return to any of the previous steps and consolidating discovered knowledge

The core of the data mining process lies in applying methods and algorithms in order to discover and extract patterns from stored data but before this step data must be pre–processed. Though, there are lots of techniques available in the data mining, few methodologies such as
1.      Artificial Neural Network
2.      Support Vector Machines
3.      Decision trees
4.      K nearest neighbor
5.      Bayesian networks
6.      Fuzzy logic
7.      Genetic Algorithm
8.      Particle Swarm Optimization
9.      Simulated Annealing


This used to obtain data from the fermentation process to be classified using ANNs .Normally there is a decrease in error probability as dimension increases, and the optimal value is reached when dimension value varies between 12 - 14, which has been proved using entropic graph algorithm.

 Reference :https://pdfs.semanticscholar.org/65aa/e95ee404c7f9b509b173a167a28cb5b232a2.pdf

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