Pollen performance modelling with an artificial neural network on commercial stone fruit cultivars
Tóm tắt
Pollen tube growth and pollen germination percentage are key factors for successful fruit set. Pollen performance is critical for the production and breeding of flowering plants and in agricultural systems in terms of fruit development. This study was carried out to predict pollen tube growth and pollen germination percentage in four stone fruits species (cherry (Prunus avium), apricot (Prunus armeniaca), plum (Prunus domestica), and peach (Prunus persica)) using a neural network. For this purpose, we measured pollen tube length and pollen germination rates under in vitro conditions. For the in vitro test, pollen grains of four stone fruit cultivars were sown in three different media and incubated at seven different temperatures for four incubation periods. A layered neural network was used for estimating the pollen germination rate and pollen tube length related to the in vitro condition. This study suggests a method for estimating the pollen germination rate and pollen tube length using artificial neural networks. The performed artificial neural networks produced an efficient prediction from in vitro data. The determination coefficients obtained between the observed and predicted data sets are 0.86 (for germination rate) and 0.81 (for tube length), indicating an accurate estimation of the in vitro data. In our case, the network that produced the best result had a 4:9:2 architecture.
Tài liệu tham khảo
Acar I, Kakani VG (2010) The effects of temperature on in vitro pollen germination and pollen tube growth of Pistacia spp. Sci Hortic 125:569–572. https://doi.org/10.1016/j.scienta.2010.04.040
Blagojevi M (2016) Web-based intelligent system for predicting apricot yields using artificial neural networks. Sci Hortic 213:125–131
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
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
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
Gofroń AG, Strzelczak A (2008) Artificial neural network models of relationships between Cladosporium spores and meteorological factors in Szczecin (Poland), Grana 47:305–315. https://doi.org/10.1080/00173130802513784
Güçlü SF, Koyuncu F (2017) Effects of relative humidity on in vitro pollen germination and tube growth in sweet cherries (Prunus Avium L.). Sci Papers Ser B Hortic 61:15–20
Hashimoto Y (1997) Applications of artificial neural networks and genetic algorithms to agricultural systems. Comput Electron Agric 18:71–72
Haykin S (2008) Neural networks and learning machines, 3rd edn. Pearson Education Inc, Upper Saddle River, p 07458
Hedhly A, Hormaza JI, Herrero M (2003) The effect of temperature on stigmatic receptivity in sweet cherry (Prunus avium L.). Plant Cell Environ 26:1673–1680
Hedhly A, Hormaza JI, Herrero M (2005) The effect of temperature on pollen germination, pollen tube growth, and stigmatic receptivity in peach. Plant Biol 7:476–483. https://doi.org/10.1055/s-2005-865850
Hormaza JI, Herrero M (1999) Pollen performance as affected by the pistilar genotype in sweet cherry (Prunus avium L.). Protoplasma 208:129–130
Janick J, Moore NJ (1996) Fruit breeding, tree and tropical fruits. Wiley, Hoboken
Kakani VG, Prasad PVV, Craufurd PQ, Wheeler TR (2002) Response of in vitro pollen germination and pollen tube growth of groundnut (Arachis hypogaea L.) genotypes to. Plant Cell Environ 25:1651–1661
Koyuncu F (2006) Response of in vitro pollen germination and pollen tube growth of strawberry cultivars to temperature 71:2000–2003
Koyuncu F, Güçlü SF (2009) Effect of temperature on in vitro pollen germination and tube growth in sweet cherries. Am-Eurasian J Agric Environ Sci 6:520–525
Lu H et al (2008) Determining optimal seeding times for tall fescue using germination studies and spatial climate analysis 148:931–941. https://doi.org/10.1016/j.agrformet.2008.01.004
Maita S, Sotomayor C (2015) The effect of three plant bioregulators on pollen germination, pollen tube growth and fruit set in almond [Prunus dulcis (Mill.) D.A. Webb] cvs. Non pareil and carmel. Electron J Biotechnol 18:381–386. https://doi.org/10.1016/j.ejbt.2015.07.004
Mi C, Yang J, Li S, Zhang X, Zhu D (2010) Prediction of accumulated temperature in vegetation period using artificial neural network. Math Comput Model 51:1453–1460. https://doi.org/10.1016/j.mcm.2009.10.005
Mladenov M, Dejanov M (2008) Application of neural networks for seed germination assessment, pp 67–72
Nabavi-Pelesaraei A (2014) Neural network modeling of energy use and greenhouse gas emissions of watermelon production systems. J the Saudi Soc Agric Sci 15:38–47
Ozesmi SL, Tan CO, Ozesmi U (2005) Methodological issues in building, training, and testing. Ecol Model 5:83–93. https://doi.org/10.1016/j.ecolmodel.2005.11.012
Reza M, Rad N, Fanaei HR (2015) Application of artificial neural networks to predict the final fruit weight and random forest to select important variables in native population of melon (Cucumis melo L.). Sci Hortic 181:108–112. https://doi.org/10.1016/j.scienta.2014.10.025
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533. https://doi.org/10.1038/323533a0
Soares JDR, Pasqual M, Lacerda WS, Silva SO, Donato SLR (2013) Utilization of artificial neural networks in the prediction of the bunches’ weight in banana plants. Sci Hortic 155:24–29
Thompson M (2004) Cherries. In: Webster AD, Looney NE (eds) Crop physiology, production and uses. CABI Publishing, Wallingford, pp 223–243
Tosun SF, Koyuncu F (2007) Investigations of suitable pollinator for 0900 Ziraat sweet cherry cv.: pollen performance tests, germination tests, germination procedures, in vitro and in vivo pollinations. Hortic Sci 34:47–53
Weibel FP, Lemcke B, Monzelio U, Giordano I, Kloss B (2012) Successful blossom thinning and crop load regulation for organic apple growing with potassium-bi-carbonate(armicarb®): results of field experiments over 3 years with 11 cultivars. Eur J Hortic Sci 77:145–153
Yoder K, Peck GM, Combs L, Byers R (2013) Using a pollen tube growth model to improve apple bloom thinning for organic production. Acta Hortic 1001:207–214