A Rule Based Classification for Vegetable Production Using Rough Set and Genetic Algorithm

R. Rathi and Debi Prasanna Acharjya
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R. Rathi: School of Information Technology and Engineering, VIT University, Vellore, India
Debi Prasanna Acharjya: School of Computer Science and Engineering, VIT University, Vellore, India

International Journal of Fuzzy System Applications (IJFSA), 2018, vol. 7, issue 1, 74-100

Abstract: This article describes how agriculture is the main occupation of India, and how the economy depends on agricultural production. Most of the land in India is dedicated to agriculture and people depend on the production of agricultural products. Therefore, forecasting the accuracy of future events based on extracted patterns plays a vital role in improving agricultural productivity. By considering the availability of micronutrients and macronutrients of the soil and water in a particular place, the growth of a plant is determined. This helps people to determine the crops to be cultivated at a certain place. In this article, the forecasting is carried out using rough sets and genetic algorithms. Rough sets are used to produce the decision rules whereas genetic algorithms are used to refine the rules and improve classification accuracy. Accuracy of the classification rules is analyzed using different selection methods and crossover operators. Results show that genetic algorithms with a roulette wheel selection and single point crossover provides better performance when compared with other existing techniques.

Date: 2018
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