Volume 23, Issue 1 (2021)
JAST 2021, 23(1): 221-234 |
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Nekoei N, Bedani M A, Khashei Siuki A. Evaluation of Adaptive Neuro-Fuzzy Inference System Models in Estimating Saffron Yield Using Meteorological Data. JAST 2021; 23 (1) :221-234
URL: http://jast.modares.ac.ir/article-23-14716-en.html
URL: http://jast.modares.ac.ir/article-23-14716-en.html
1- Faculty of Agriculture, University of Birjand, Islamic Republic of Iran.
2- Saffron Research Group, Faculty of Agriculture, University of Birjand, Islamic Republic of Iran.
3- Water Engineering Department, Faculty of Agriculture, University of Birjand, Islamic Republic of Iran.
2- Saffron Research Group, Faculty of Agriculture, University of Birjand, Islamic Republic of Iran.
3- Water Engineering Department, Faculty of Agriculture, University of Birjand, Islamic Republic of Iran.
Abstract: (1425 Views)
Saffron is one of the most valuable agricultural and medicinal plants of the world and has a special place in Iran's export of products. Presently, Iran is the world's largest producer and exporter of saffron and more than 93/7% of the world production belongs to Iran. However, despite the long history of saffron cultivation and its value-added in comparison to many of the other crops in the country, a lower share of new technologies is assigned to it, and its production is mainly based on local knowledge. This study aimed to develop and evaluate the performance of Adaptive Neuro-Fuzzy Inference System model (ANFIS) in calculating the yield of saffron using meteorological data from 20 synoptic stations in the province, including evapotranspiration, temperature (maximum, minimum), the mean relative humidity, and rainfall. To this end, by using software Wingamma, data and parameters were analyzed and the best combinations of inputs to the model were determined. In order to assess the models, statistical parameters of correlation coefficient, the mean absolute error, and mean square error were used to predict the performance of the plant. ANFIS model was most effective when the data of total minimum temperature, precipitation, evapotranspiration, and relative humidity of autumn were used as independent variables for forecasting yield (R2= 0.5627, RMSE= 2.051 kg ha-1, and MAE = 1.7274 kg ha-1) .
Article Type: Research Paper |
Subject:
Agricultural Economics/Agriculture Marketing and Supply Chains
Received: 2017/04/27 | Accepted: 2020/09/19 | Published: 2020/12/27
Received: 2017/04/27 | Accepted: 2020/09/19 | Published: 2020/12/27
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