Akbarpour, A., Khorashadizadeh, O., Shahidi, A., and Ghochanian, E. 2013. Performance evaluation of artificial neural network models in estimate production of yield saffron based on climate parameters. Journal of Saffron Research 1 (1): 27-35.
Behdani, M.R., Koochaki, A., Nasiri, M., and Rezvani, P. 2006. Evaluation of quantitative relationships between saffron yield and nutrition (on farm trial). Iranian Journal of Crop Research 3 (1): 1-14. (In Persian).
Behdani, M.A., Jami-alahmadi, M., and Akbarpoor, A. The evaluation of plant effective indices on growth and production of saffron agro ecosystem in Southern Khorasan. Acta Horticulture 850: 179-184.
Chaji, N., Khorassani, R., Astaraei, A., and Lakzian, A. 2013. Effect of phosphorous and nitrogen on vegetative growth and production of daughter corms of saffron. Journal of Saffron Research 1: 1-12.
Cybenko, G. 1989. Dynamic load balancing for distributed memory multiprocessors. Journal of Parallel and Distributed Computing 7 (2): 279-301.
Dole, J.M., and Wilkins, H.F. 1999. Floriculture principles and species. Prentice Hall 537-545.
Fageria, N.K., Moreira, A., and Dos Santos, A.B. 2013. Phosphorus uptake and use efficiency in field crops. Journal of Plant Nutrition 36 (13): 2013-2022.
Fausett, L.V. 1994. Fundamentals of neural networks: architectures, algorithms, and applications. Englewood Cliffs: Prentice-Hall, 329p.
Gonzalez-Fernandez, I., Iglesias-Otero, M.A., Esteki, M., Moldes, O.A., Mejuto, J.C., and Simal-Gandara, J.A. 2018. Critical review on the use of artificial neural networks in olive oil production, characterization and authentication. Critical Reviews in Food Science and Nutrition pp.1-14.
Haynes, R.J., and Naidu, R. 1998. Influence of lime, fertilizer and manure applications on soil organic matter content and soil physical conditions: Nutrient Cycling in Agroecosystems 51: 123-137.
Hesse, P.R. 1971. A Text Book of Soil Chemical Analysis. John Murray. London.
Jain, S.K., Nayak, P.C., and Sudheer, K.P. 2008. Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrological Processes 22 (13): 2225-2234.
Khadempour, F., KhasheiSiuki, A., and Behdani, M.A. 2020. Evaluation of the efficiency of Lazy algorithm in estimating yield of Saffron based on climatic parameters. Saffron Agronomy and Technology 8 (2): 295-304.
Knudsen, D., Peterson, G.A., and Pratt, P.F. 1982. Lithium, sodium, and potassium. Methods of soil analysis. Part 2. Chemical and Microbiological Properties pp. 225-246.
Koocheki, A., Rezvani-Moghaddam, P., and Fallahi, H.R. 2016. Effects of planting dates, irrigation management and cover crops on growth and yield of saffron (Crocus sativa L.). Journal of Agroecology 8 (1): 435-451. (In Persian with English Summary).
Koocheki, A., and Seyyedi, S.M. 2015. Relationship between nitrogen and phosphorus use efficiency in saffron (Crocus sativus L.) as affected by mother corm size and fertilization. Industrial Crops and Products 71: 128-137.
Kouzegaran, S., Baygi, M.M., Babaeian, I., and Khashei-Siuki, A. 2020. Modeling of the saffron yield in Central Khorasan region based on meteorological extreme events. Theoretical and Applied Climatology 139 (3):1207-1217.
Munshi, A.M. 1994. Effect of N and К on the floral yield and corn production in saffron under rain-fed condition. Indian Journal of Cocoa, Arecanut Spices 18: 24-44.
Nadian, H., Nateghzadwh, B., and Jafari, S. 2012. Effect of salinity and nitrogenfertilizers on some quantity and quality parameters of sugarcane (Saccharum sp.). Journal of Food Agriculture and Environment 10: 470-474.
Naghizadeh, M., Gholami-shabestari, M., and Shamsaddin-saied, M. 2014. The study of some physiological responses of three Iranian saffron (Crocus sativus L.) landraces to salinity stress. Saffron Agronomy and Technology 2 (2): 127-136. (In Persian).
Niazian, M., Sadat-Noori, S.A., and Abdipour, M. 2018. Modeling the seed yield of Ajowan (Trachyspermum ammi L.) using artificial neural network and multiple linear regression models. Industrial Crops and Products 117: 224-234.
Ranjbar, A., Emami, E., Karimi Karouyeh, A., and Khorassani, R. 2015. Determining the most important soil properties affecting the yield of Saffron in the Ghayenat area. Journal of Water and Soil 29 (3): 673-682. (In Persian).
Rashed-Mohsel, M.H., Azizi, J., and Sabet-Teimouri, M. 2006. Examination of Crocus sativus L. response to organic and inorganic fertilizers. The Second International Symposium on Technology and Agronomy of Crocus sativus, L-Mashhad- Iran. (In Persian).
Rezvani-Moghaddam, P., Khorramdel, S, and Mollafilabi, A. 2015, Evaluation of soil physical and chemical characteristics impacts on morphological criteria and yield of saffron (Crocus sativus L.), Journal of Saffron Research 3 (2): 188-203. (In Persian).
Riahi-Modavar, H., Khashei-Siuki, A., and Seifi, A. 2017. Accuracy and uncertainty analysis of artificial neural network in predicting saffron yield in the south Khorasan province based on meteorological data. Saffron Agronomy and Technology 5 (3): 55-71. (In Persian).
Sabet-Teimouri, M., Kafi, M., Avarseji, Z., and Orooji, K. 2010. Effect of drought stress, corm size and corm tunic on morphoecophysiological characteristics of saffron (Crocus sativus L.) in greenhouse conditions. Agroecology 2 (2): 323-334. (In Persian).
Salari, A., Bashiri, M., Maroosi, A. 2017. forecasting saffron yield using data mining and determining climate parameters influencing its yield in the province of Khorasan Razavi. Journal of Saffron Research 5 (1): 1-17. (In Persian).
Shahandeh, H., and Mousavi, M.A. 1998. ssessment of physico-chemical properties of soil in relation to water and Crocus sativus L. in Gonabad. Khorasan Researech Center. (In Persian).
Shirdeli, A., and Tavassoli, A. 2015. Predicting yield and water use efficiency in saffron using models of artificial neural network based on climate factors and water. Saffron Agronomy and Technology 3 (2): 121-131. (In Persian).
Singh, H. 2017. Development of a crop yield prediction model for corn using an artificial neural network and high resolution remotely sensed imagery. Doctoral Dissertation, McGill University Montreal (Quebec), Canada.
Temperini, O., Rea, R., Temperini, A., Colla, G., and Rouphael, Y. 2009. Evaluation of saffron (Crocus sativus L.) production in Italy: Effects of the age of saffron fields and plant density. Journal of Food, Agriculture and Environment 7 (1): 19-23.
Tietje, O., and Hennings, V. 1996. Accuracy of the saturated hydraulic conductivity prediction by pedotransfer functions compared to the variability within FAO textural classes. Geoderma 69: 71-84.
Torkashvand. A.M., Ahmadi, A, and Nikravesh, N.L. 2017. Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions [MLR). Journal of Integrative Agriculture 16 (7): 1634-1644.
Zabihi, H., and Feizi., H. 2014. Saffron Response to the Rate of Two Kinds of Potassium Fertilizer', Saffron Agronomy and Technology 2 (3): 191-198. (In Persian).