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Crop suitability prediction in Vellore District using rough set on fuzzy approximation space and neural network

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Abstract

In Indian economy, agriculture is the prime vocation that avails in the overall development of the country. Tamil Nadu occupies approximately 7% of the nation's population, with 3% of water resources and 4% of land resources at the country level. The crop suitability prediction is of prime importance to enhance the nutritional security to the developing country. Based on several crops grown in a particular place, and the availability of natural resources, one can identify the suitability of crops that can be grown in a particular place. To this end, many mathematical tools were developed, but they failed to include processing of uncertainties present in the accumulated data. Therefore, in this paper an effort has been made to process the uncertainties by hybridizing rough set on fuzzy approximation space and neural network. The rough set on fuzzy approximation space identifies the almost indiscernibility among the natural resources and helps in minimizing the computational procedure on employing data reduction techniques, whereas neural network helps in prediction process. The proposed model is analysed on agriculture data of Vellore District of Tamil Nadu, India, and achieved 93% of classification accuracy in validation. The model is compared with an earlier model and achieved 8% more accuracy while predicting unseen associations.

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Authors and Affiliations

  1. School of Information Technology and Engineering, VIT University, Vellore, Tamilnadu, India

    A. Anitha

  2. School of Computing Science and Engineering, VIT University, Vellore, Tamilnadu, India

    D. P. Acharjya

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  1. A. Anitha
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  2. D. P. Acharjya
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Correspondence to D. P. Acharjya.

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Anitha, A., Acharjya, D.P. Crop suitability prediction in Vellore District using rough set on fuzzy approximation space and neural network. Neural Comput & Applic 30, 3633–3650 (2018). https://doi.org/10.1007/s00521-017-2948-1

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  • DOI: https://doi.org/10.1007/s00521-017-2948-1

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