Pollen performance modelling with an artificial neural network on commercial stone fruit cultivars
Author:
Publisher
Springer Science and Business Media LLC
Subject
Horticulture,Plant Science,Biotechnology
Link
http://link.springer.com/content/pdf/10.1007/s13580-019-00208-7.pdf
Reference29 articles.
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2. Blagojevi M (2016) Web-based intelligent system for predicting apricot yields using artificial neural networks. Sci Hortic 213:125–131
3. Bolat I, Pırlak L (1999) An investigation on pollen viability, germination and tube growth in some stone fruits. Turk J Agric For 23:383–388
4. Chen YPP, Ivanova EP, Wang F and Carloni P (2010) 9.15—Bioinformatics. In Ben Liu HW, Mander L (eds) Comprehensive natural products {II}, pp 569–593. https://doi.org/10.1016/B978-008045382-8.00729-2
5. Delong CN, Yoder KS, Combs L, Veilleux RE, Peck GM (2016) Apple pollen tube growth rates are regulated by parentage and environment. J Am Soc Hortic Sci 141:548–554. https://doi.org/10.21273/JASHS03824-16
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