Agriculture Crop Suitability Prediction Using Rough Set on Intuitionistic Fuzzy Approximation Space and Neural Network. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Anitha, A.; Acharjya, D. P.
• Is Part Of: Fuzzy information and engineering. Volume 11:Issue 1(2019)
• Page Start: 64
• Page End: 85
• Publication Date: 2019-01-02
• Subjects: Almost indiscernible -- intuitionistic fuzzy proximity relation -- neural network -- knowledge discovery -- prediction -- rough set; Fuzzy systems -- Periodicals; Intelligent control systems -- Periodicals; 629.89
• DOI: 10.1080/16168658.2021.1886813
• Languages: English
• ISSNs: 1616-8658

Abstract

Abstract : Agriculture plays a vital role in Indian economy. On considering the overall geographical space verses population in India, 7% of population is chronicled in Tamilnadu, with 3% of water and 4% of land resources. Thus an automated prediction system becomes essential for predicting the crop based on the nutritional security of the country. In this paper, effort has been made to process the uncertainties by hybridizing rough set on intuitionistic fuzzy approximation space (RSIFAS) [Acharjya DP, Tripathy BK. Rough sets on intuitionistic fuzzy approximation spaces and knowledge representation. Int J Artif Int Comput Res. 2009;1 (1):29–36.] and neural network [Hecht NR. Theory of the backpropagation neural network. Proceedings of the international Joint Conference on neural networks, 1 (1989), 593–605.]. RSIFAS identifies the almost indiscernibility among the natural resources, and helps in reducing the computational procedure on employing data reduction techniques whereas neural network helps in prediction process. It helps to find the crops that may be cultivated based on the available natural resources. The proposed model is analyzed on data accumulated from Vellore district of Tamilnadu, India and achieved 93.7% of average classification accuracy. The model is compared with earlier models and found 6.9% better accuracy while prediction.