Comparison of Self organizing maps and Sammon's mapping on agricultural datasets for precision agriculture

Abstract

Over the ages technology has been occupying every field including the agriculture. Precision agriculture and Visual data mining uses technology to apply specific principles of data to interpret details like when and how much fertilizers to be used in a particular area (land). Data mining is the process of detecting patterns in a clustering form (certain chunk of information) to get more precise and accurate information. Not only it provides facilitating results but also improves the efficiency of farmer's productivity, it has also helped in qualitative improvement in the overall quality of life by providing timely and data inputs for decision making. Raw data collected from the statistics analysis has helped in determining the data to its full extent. This paper shows the techniques applied to real and till date agriculture data to reduce the high dimensional input data to much smaller size. We are using Self-organizing maps and multi-dimensional scaling techniques (Sammon's mapping) to reduce the data.

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