Prediction of Saffron Yield Based on Soil Properties Using Artificial Neural Networks as a Way to Identify Susceptible Lands of Saffron

References

• Knudsen, D., G. A. Peterson, and P. F. Pratt. 1983. Lithium, sodium, and potassium. Methods of soil analysis. Part 2. Chemical and microbiological properties 9: 225–46.
   Google Scholar
• Aghhavani Shajari, M., P. Rezvani Moghaddam, A. Koocheki, H. R. Fallahi, and R. Taherpour Kalantari. 2015. Evaluation of the effects of soil texture on yield and growth of saffron (Crocus sativus L.). Saffron Agronomy and Technology 2 (4):311–22. [In Persian].
   Google Scholar
• Behdani, M. A., M. Jami-alahmadi, and A. Akbarpoor. 2010. The evaluation of plant effective indices on growth and production of saffron agro ecosystem in Southern Khorasan. Acta Horticulture 850:179–84. doi:10.17660/ActaHortic.2010.850.29.
   Google Scholar
• Behdani, M. R., A. Koochaki, M. Nasiri, and P. Rezvani. 2006. Evaluation of quantitative relationships between saffron yield and nutrition [on farm trial. Iranian Journal of Crop Research 3 (1):1–14. [In Persian].
   Google Scholar
• Brewster, J. L., and H. A. Butler. 1989. Effects of nitrogen supply on bulb development in onion Allium cepa L. Journal of Experimental Botany 40:1155–62. doi:10.1093/jxb/40.10.1155.
   Web of Science ®Google Scholar
• Chaji, N., R. Khorassani, A. Astaraei, and A. Lakzian. 2013. Effect of phosphorous and nitrogen on vegetative growth and production of daughter corms of saffron. Journal of Saffron Research 1:1–12.
   Google Scholar
• Cybenko, G. 1989. Dynamic load balancing for distributed memory multiprocessors. Journal of Parallel and Distributed Computing 7 (2):279–301. doi:10.1016/0743-7315(89)90021-X.
   Web of Science ®Google Scholar
• Dole, J. M., and H. F. Wilkins. 1999. Floriculture: principles and species. USA: Prentice Hall Inc, 613 pp.
   Google Scholar
• El-Desuki, M., M. Hafez, A. Mahmoud, and A. El-Al. 2010. Effect of organic and biofertilizer on the plant growth, green pod yield, quality of pea. International Journal of Academic Research 2:87–92.
   Google Scholar
• Fageria, N. K., A. Moreira, and A. B. Dos Santos. 2013. Phosphorus uptake and use efficiency in field crops. Journal of Plant Nutrition 36 (13):2013–22. doi:10.1080/01904167.2013.816728.
   Web of Science ®Google Scholar
• Fausett, L. V. 1994. Fundamentals of neural networks: Architectures, algorithms, and applications, 3. Englewood Cliffs: Prentice-Hall.
   Google Scholar
• Gonzalez-Fernandez, I., M. A. Iglesias-Otero, M. Esteki, O. A. Moldes, J. C. Mejuto, and J. A. Simal-Gandara. 2018. Critical review on the use of artificial neural networks in olive oil production, characterization and authentication. Critical Reviews in Food Science and Nutrition, 59 (12):1913–26.
   PubMed Web of Science ®Google Scholar
• Haynes, R. J., and R. Naidu. 1998. Influence of lime, fertilizer and manure applications on soil organic matter content and soil physical conditions. Nutrient Cycling in Agroecosystems 51:123–37. doi:10.1023/A:1009738307837.
   Web of Science ®Google Scholar
• Hesse, P. R.A. Hesse. 1971. Textbook of soil chemical analysis. New York; 520 pp.
   Google Scholar
• Jain, S. K., P. C. Nayak, and K. P. Sudheer. 2008. Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrological Processes 22 (13):2225–34. doi:10.1002/hyp.6819.
   Web of Science ®Google Scholar
• Khosravan, V. 2002. Anticonvulsant effects of aqueous and ethanolic extracts of Crocus sativus L. stigmas in mice. Archives of Iranian Medicine 5:44.
   Google Scholar
• Koocheki, A., P. Rezvani Moghaddam, and H. R. Fallahi. 2016. Effects of planting dates, irrigation management and cover crops on growth and yield of saffron (Crocus sativa L). Journal of Agroecology, 8 (1):435–51. [In Persian with English Summary].
   Google Scholar
• Koocheki, A., and S. M. Seyyedi. 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 71:128–37. doi:10.1016/j.indcrop.2015.03.085.
   Web of Science ®Google Scholar
• 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.
   Google Scholar
• Nadian, H., B. Nateghzadwh, and S. Jafari. 2012. Effect of salinity and nitrogenfertilizers on some quantity and quality parameters of sugarcane (Saccharumsp.). Journal of Food Agriculture. Environment 10:470–74.
   Google Scholar
• Naghizadeh, M., M. Gholami Shabestari, and M. Shamsaddin Saied. 2014. The study of some physiological responses of three Iranian saffron (Crocus sativus L.) landraces to salinity stress. Saffron Agronomy and Technology 2 (2):127–36. [In Persian].
   Google Scholar
• Niazian, M., S. A. Sadat-Noori, and M. Abdipour. 2018. Modeling the seed yield of Ajowan (Trachyspermum ammi L.) using artificial neural network and multiple linear regression models. Industrial Crops and Products 117:224–34. doi:10.1016/j.indcrop.2018.03.013.
   Web of Science ®Google Scholar
• Rashed Mohsel, M. H., J. Azizi, and M. Sabet Teimouri. 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].
   Google Scholar
• Rezvani Moghaddam, P., S. Khorramdel, and A. Mollafilabi. 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].
   Google Scholar
• Riahi Modavar, H., A. Khashei-Siuki, and A. Seifi. 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].
   Google Scholar
• Rostami, M., B. Mohmmad Parast, and R. Golfam. 2015. The effect of different levels of salinity stress on some physiological characteristics of saffron (Crocus Sativus L.). Saffron Agronomy and Technology 3 (3):177–93. [In Persian].
   Google Scholar
• Sabet Teimouri, M., M. Kafi, Z. Avarseji, and K. Orooji. 2010. Effect of drought stress, corm size and corm tunic on morphoecophysiological characteristics of saffron (Crocus sativus L.) in greenhouse conditions. Agroecology 2 (2):323–34. [In Persian].
   Google Scholar
• Salari, A., M. Bashiri, and A. Maroosi. 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].
   Google Scholar
• Shahandeh, H., and M. A. Mousavi. 1988. Evaluation of physical and chemical properties of soils in relation to water and saffron in GONABAD, Research Projects, Scientific Research and Industrial Organizations of Khorasan, Iran, 150 p. (In Persian).
   Google Scholar
• Shirdeli, A., and A. Tavassoli. 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):121–31. [In Persian].
   Google Scholar
• 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)
   Google Scholar
• Sunanda, B. P. V., B. Rammohan, K. Amitabh, and B. L. Kudagi. 2014. The effective study of aqueous extract of crocus sativus linn. In chemical induced convulsants in rats. World Journal Pharmacy and Pharmaceutical Science 3:1175–82.
   Google Scholar
• Temperini, O., R. Rea, A. Temperini, G. Colla, and Y. Rouphael. 2009. Evaluation of saffron (Crocus sativus L.) production in Italy: Effects of the age of saffron fields and plant density. Journal of Food, Agriculture & Environment 7 (1):19–23.
   Google Scholar
• Tietje, O., and V. Hennings. 1996. Accuracy of the saturated hydraulic conductivity prediction by pedotransfer functions compared to the variability within FAO textural classes. Geoderma 69:71–84. doi:10.1016/0016-7061(95)00050-X.
   Web of Science ®Google Scholar
• Torkashvand, A. M., A. Ahmadi, and N. L. Nikravesh. 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):1634–44. doi:10.1016/S2095-3119(16)61546-0.
   Web of Science ®Google Scholar
• Zabihi, H., and H. Feizi. 2014. Saffron response to the rate of two kinds of potassium fertilizer’. Saffron Agronomy and Technology 2 (3):191–98. [In Persian].
   Google Scholar