Agriculture Crop Suitability Prediction Using Rough Set on Intuitionistic Fuzzy Approximation Space and Neural Network

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.

KEYWORDS:

• Almost indiscernible
• intuitionistic fuzzy proximity relation
• neural network
• knowledge discovery
• prediction
• rough set

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

A. Anitha

Dr. A. Anitha is an Associate Professor in the School of Information Technology at VIT, Vellore, India. She received the MCA degree from Adhi Parasakthi College of Science, Kalavai, Tamil Nadu, India. She has published many international journal and conference papers to her credit. Her research interest includes data mining, fuzzy logic, neural network and rough sets. She is associated with the professional bodies CSI.

D. P. Acharjya

Dr. D. P. Acharjya is a Professor in the School of Computing Sciences and Engineering at VIT, Vellore, India. He received his MSc from NIT, Rourkela, India; M. Tech. in Computer Science from Utkal University, India; and PhD in Computer Science from Berhampur University, India. He has been awarded the Gold Medal in M. Sc.; Eminent Academician Award; Outstanding Educator and Scholar Award; The Best Citizens of India Award; and Bharat Vikas Award from various organizations of India. He has authored 84 international and national journal and conference papers. Besides, he has published 4 books and 17 book chapters with international publishers. In addition, he has edited 7 books with international publishers like CRC Press; Springer; and IGI Global, USA. His research interest includes rough sets, knowledge representation, machine learning, bio-inspired computing, and business intelligence. He is associated with many professional bodies, such as ACM, IACSIT, IAENG, CSTA, IRSS, CSI, ISTE, OITS, ISIAM, IMS, and AMTI.