Wheat yield forecasting using fuzzy logic Online publication date: Fri, 06-Apr-2018
by Bindu Garg; Tanya Sah; Shubham Aggarwal
International Journal of Convergence Computing (IJCONVC), Vol. 3, No. 1, 2018
Abstract: Forecasting in general and crop yield forecasting, in particular, is considered a compound problem. Food and Agriculture Organisation's report Global Agriculture Towards 2050 accentuates the challenges which agriculture sector is going to face in the near future. The report draws the attention towards the disparity between the demand and supply. It also highlights the issues that automation has introduced into the food and agriculture sector. Where there is a need to increase the production to feed the rapidly increasing population there is an equal necessity for coping with the dwindling numbers of farming workers. As such there is a need for a reliable forecasting algorithm capable of handling time series data. In this paper, we have proposed a fuzzy time series forecasting algorithm to forecast wheat yield. The reason to choose fuzzy over other forecasting methods is its capability of dealing with the vague, imprecise data and it outperforms many statistical conventional models in such conditions. Neural network has been used for training and defuzzification of the forecasted values. To attests the efficacy and the performance of the proposed method, it has been tested against the wheat production dataset.
Online publication date: Fri, 06-Apr-2018
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