Article: Wheat yield forecasting using fuzzy logic Journal: International Journal of Convergence Computing (IJCONVC) 2018 Vol.3 No.1 pp.35 - 47 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. Inderscience Publishers - linking academia, business and industry through research

Title: Wheat yield forecasting using fuzzy logic

Authors: Bindu Garg; Tanya Sah; Shubham Aggarwal

Addresses: Computer Science Department, Bharati Vidyapeeth's College of Engineering, New Delhi, India ' Computer Science Department, Bharati Vidyapeeth's College of Engineering, New Delhi, India ' Computer Science Department, Bharati Vidyapeeth's College of Engineering, New Delhi, India

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.

Keywords: fuzzy logic; wheat yield forecasting; neural network; soft computing; fuzzy time series; linguistic values; fuzzy relationships; crop yield forecasting; AFER; forecasting; prediction.

DOI: 10.1504/IJCONVC.2018.091114

International Journal of Convergence Computing, 2018 Vol.3 No.1, pp.35 - 47

Received: 29 Jun 2017
Accepted: 27 Sep 2017
Published online: 10 Apr 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Wheat yield forecasting using fuzzy logic

Keep up-to-date