Prediction of Crop Yields Based on Fuzzy Rule-Based System (FRBS) Using the Takagi Sugeno-Kang Approach

Author:

Borse Kalpesh,Agnihotri Prasit G.

Publisher

Springer International Publishing

Reference12 articles.

1. Agrawal, R., Jain, R.C., Mehta, S.C.: Yield forecast based on weather variables and agricultural inputs on agro-climatic zone basis. Ind. J. Agri. Sci. 71(7), 487–490 (2001)

2. Huda, A.K.S., Ghildyal, B.P., Tomar, V.S., Jain, R.C.: Contribution of climate variables in predicting rice yield. Agric. Met. 15, 71–86 (1975)

3. Chowdhury, A., Sarkar, M.B.: Estimation of rice yield through weather factors in a dry sub-humid region. Mausam 32(4), 393–396 (1981)

4. Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and soft computing, a computational approach to learning and machine intelligence. Prentice Hall, NJ, USA (1997). ISBN 0-13-261066-3

5. Loukas, Y.L.: Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor for improved QSAR studies. J. Med. Chem. 44(17), 2772–2783 (2001)

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