Mathematics and Statistics Vol. 8(4), pp. 451 - 457
DOI: 10.13189/ms.2020.080412
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Comparative Study on Fuzzy Models for Crop Production Forecasting

Amit Kumar Rana *
Department of Mathematics, Swami Vivekanand Subharti University, Meerut, India


Fuzzy sets theory is a very useful technique to increase effectiveness and efficiency of forecasting. The conventional time series is not applicable when the variable of time series are words variables i.e. variables with linguistic terms. As India and most of the Asian countries are of agriculture-based economy with very smaller farmer land holding area in comparison to America, Australia and Europe counterparts, it becomes more important for these countries to have an approximate idea regarding future crop production. It not only will help in planning policies for future but also will be a great help for farmers and agro based companies for their future managements. For small area production, soft computing technique is an important and effective tool for predicting production, as agriculture production involve a high degree of uncertainties in many parameters. In the present study, 21 years agricultural crop yield data is used and a comparative analysis of forecast is done with three fuzzy models. The robustness of the model is tested on real time agricultural farm production data of wheat crop of G.B. Pant University of Agriculture and Technology Pantnagar, India. As soft computing techniques involve uncertainty of the system under study, it becomes more and more important for forecasting models to be accurate with the prediction. The efficiency of the three models is examined on the basis of statistical errors. The models under study are judged on the basis of Mean Square Error and average percentage error. The results of the study are in case of small area production prediction and will encourage for predicting large scale production.

Fuzzy Logical Relations (FLR), Fuzzy Sets (FS), Fuzzy Time Series (FTS), Time Invariant Model (TIM)

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Amit Kumar Rana , "Comparative Study on Fuzzy Models for Crop Production Forecasting," Mathematics and Statistics, Vol. 8, No. 4, pp. 451 - 457, 2020. DOI: 10.13189/ms.2020.080412.

(b). APA Format:
Amit Kumar Rana (2020). Comparative Study on Fuzzy Models for Crop Production Forecasting. Mathematics and Statistics, 8(4), 451 - 457. DOI: 10.13189/ms.2020.080412.