Comparison between artificial neural networks and some mathematical models in leaf area estimation of Red Chief apple variety

Year 2020, , 15 - 20, 01.04.2020

Selma Boyacı , Hande Küçükönder

https://doi.org/10.29136/mediterranean.634614

Cited By: 1

Abstract

Leaf area index is an important variable in ecological and physiological studies. This study was aimed to determine the most suitable model explaining the leaf area estimation and weekly growth of leaf parameters in Red Chief apple variety. In the first part of the study, the leaf area was modeled through two different models (Model-1 and Model-2) developed based on ANN and power function (LA= AxB). In the second part, the weekly growth of each of the leaf width, length and area parameters were analyzed according to the Gompertz and Logistics function. The results of analysis revealed that leaf area estimations performed by ANN (Training: R2= 0.98, RMSE= 0.922, MAD= 0.614, MAPE= 4.22; Testing: R2= 0.94, RMSE= 3.346 MAD= 1.889 MAPE= 4.88) were more successful than Model-1 and Model-2. In addition, Gompertz has come to the fore as the model that best describes the weekly growth in all leaf parameters (Width: R2= 0.98, RMSE= 0.154, MAD= 0.134, MAPE= 3.65, Length: R2= 0.98, RMSE= 0.180, MAD= 0.145, MAPE= 2.26 and Leaf area: R2= 0.99, RMSE= 0.73, MAD= 0.654, MAPE= 4.60).

Keywords

ANN, Leaf area index, Growth, Apple, Mathematical model

Red Chief elma çeşidinde yapay sinir ağları ve bazı matematiksel modeller kullanılarak yaprak alan tahminlerinin karşılaştırılması

Abstract

Yaprak alan indeksi ekolojik ve fizyolojik çalışmalarda önemli bir değişkendir. Çalışmada, Red Chief elma çeşidinde yaprak alan tahmini ve yaprak parametrelerinin haftalık büyümesini açıklayan en uygun modelin belirlenmesi amaçlanmıştır. Bu amaçla çalışmanın ilk kısmında ANN ve power fonksiyonuna (LA= AxB) dayalı geliştirilen iki farklı model (Model-1 ve Model-2) aracılığıyla yaprak alanı modellenmekte, ikinci kısmında yaprak en, boy ve alan parametrelerinin her birinin haftalık büyümeleri Gompertz ve Lojistik fonksiyona göre analiz edilmektedir. Analiz sonuçlarına göre yaprak alan tahmininde ANN’nin (Eğitim: R2= 0.98, RMSE= 0.922, MAD= 0.614, MAPE= 4.22; Test: R2= 0.94, RMSE= 3.346, MAD= 1.889, MAPE= 4.88) Model-1 ve Model-2’den daha başarılı tahminlerde bulunduğu gözlemlenmiştir. Bunun yanında yaprak parametrelerinin tamamında haftalık büyümeyi en iyi açıklayan modelin Gompertz olduğu (En: R2= 0.98, RMSE= 0.154, MAD= 0.134, MAPE= 3.65, Boy: R2= 0.98, RMSE= 0.180, MAD= 0.145, MAPE= 2.26 ve Yaprak alanı: R2= 0.99, RMSE= 0.73, MAD= 0.654, MAPE= 4.60) görülmüştür.

Keywords

ANN, Yaprak alan indeksi, Büyüme, Elma, Matematiksel model

References

• Akıllı A, Atıl H (2014) Artificial intelligence technology, fuzzy logic and artificial neural networks in dairy. Animal Production 55(1): 39-45.
• Akkol S, Akıllı A, Cemal İ (2017) Comparison of artificial neural network and multiple linear regression for prediction of live weight in hair goats. Yuzuncu Yıl University Journal of Agricultural Science 27: 21-29.
• Celik H, Odabas MS, Odabas F (2011) Leaf area prediction models for highbush blueberries (Vaccinium corymbosum L.) from linear measurements. Advances in Food Sciences 33:16-21.
• Cho YY, Oh S, Oh MM, Son JE (2007) Estimation of individual leaf area, fresh weight, and dry weight of hydroponically grown cucumbers (Cucumis sativus L.) using leaf length, width, and SPAD value. Scientia Horticulturae 111: 330-334.
• Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal 43(1): 47-66.
• De Swart EAM, Groenwold R, Kanne HJ, Stam P, Marcelis LFM, Voorrips RE (2004) Non-destructive estimation of leaf area for different plant ages and accessions of Capsicum annuum L.. Journal of Horticultural Science & Biotechnology 79(5): 764-770.
• Demirsoy H, Demirsoy L (2003) A validated leaf area prediction model for some Cherry cultivars in Turkey. Pakistan Journal of Botany 35(3): 361-367.
• Keramatlou I, Sharifani M, Sabouri H, Alizadeh M, Kamkar BA (2015) Simple linear model for leaf area estimation in Persian walnut (Juglans regia L.). Scientia Horticulturae 184: 36-39.
• Kıymaz S, Karadavut U, Ertek A (2018a) A comparison of artificial neural networks and some nonlinear models of leaf area estimation of sugar beet at different nitrogen levels. Turkish Journal of Agriculture and Natural Sciences 5(3): 303-309.
• Kıymaz S, Karadavut U, Şimşek G, Soğancı K (2018b) Comparison of some mathematical growth models for leaf area development of some beans (Phaseolus vulgaris L.) grown under different irrigation regimes. Journal of Süleyman Demirel University Faculty of Agriculture, 1st International Agricultural Structures and Irrigation Congress, Special Issue: 166-172.
• Küçükönder H, Boyacı S, Akyüz A (2016) A modeling study with an artificial neural network: developing estimation models for the tomato plant leaf area. Turkish Journal Of Agriculture and Forestry. 40: 203-212.
• Kumar R (2009) Calibration and validation of regression model for non-destructive leaf area estimation of saffron (Crocus sativus L.). Scientia Horticulturae 122: 142-145.
• Maren AJ, Harston CT, Pap RM (1990) Handbook of neural computing applications. Academic Press, eBook ISBN: 9781483264844, San Diego (CA).
• Mohsenin NN (1986) Physical properties of plant and animal materials. Gordon and Breach Science Publishers, New York, NY, USA.
• Montero FJ, De Juan JA, Cuesta A, Brasa A (2000) Nondestructive methods to estimate leaf area in Vitis vinifera L.. HortScience 35: 696-698.
• Moosavi AA, Sepaskhah A (2012) Artificial neural networks for predicting unsaturated soil hydraulic characteristics at different applied tensions. Archives Agronomy and Soil Science 58(2): 125-153.
• Özşahin Ş, Singer H (2019a) Estimation of the surface roughness and adhesion resistance of wood with artificial neural networks. Journal of Politeknik 22(4): 889-900.
• Özşahin Ş, Singer H (2019b) Using an artificial neural network model to estimate wood surface roughness. Düzce University Journal of Science and Technology 7(3): 1764-1777.
• Öztemel E (2016) Artifical neural networks, Papatya Press, İstanbul, Turkey.
• Ozturk A, Cemek B, Kucuktopcu E (2019) Modelling of the leaf area for various pear cultivars using neuro computing approaches. Spanish Journal of Agricultural Research 17(4): 1-11.
• Palmer JW (1987) The measurement of leaf area in apple trees. Journal of Horticultural Science 62: 5-10.
• Pandey SK, Singh H (2011) A simple, cost-effective method for leaf area estimation. doi:10.1155/2011/658240.
• Peksen E (2007) Non-destructive leaf area estimation model for faba bean (Vicia faba L.). Scientia Horticulturae 113: 322-328.
• Rivera CM, Rouphael Y, Cardarelli M, Colla G (2007) A simple and accurate equation for estimating individual leaf area of eggplant from linear measurements. European Journal of Horticultural Science 72(2): 228-230.
• Rouphael Y, Mouneimne AH, Mendoza-de Gyves E, Rivera CM, Colla G (2010) Modeling individual leaf area of rose (Rosa hybrida L.) based on leaf length and width measurement. Photosynthetica 48(1): 9-15.
• Sala F, Arsene GG, Iordănescu O, Bodea M (2015) Leaf area constant modeling optimizing foliar area measurement in plants: A case study in apple tree. Scientia Horticulturae 193: 218-224.
• Schwarz D, Kläring HP (2001) Allometry to estimate leaf area of tomato. Journal of Plant Nutrition 24(8): 1291-1309.
• Serdar Ü, Demirsoy H (2006) Non-destructive leaf area estimation in chestnut. Scientia Horticulturae 108: 227-230.
• Sérgio P, Silva L, Barbin D, Gonçalves RJS, Firmino JDC, Fonseca IC (2004) Leaf area estimates of custard apple tree progenies. Revista Brasileira de Fruticultura 26(3): 558-560.
• Shabani A, Ghaffary KA, Sepaskhah AR, Kamgar-Haghighi AA (2017) Using the artificial neural network to estimate leaf area. Scientia Horticulturae 216: 103-110.
• Takma Ç, Atıl H, Aksakal V (2012) Comparison of multiple linear regression and artificial neural network models goodness of fit to lactation milk yields. Journal of Kafkas University Veterinary Faculty 18(6): 941-944.
• Vazquez-Cruz MA, Jimenez-Garcia SN, Luna-Rubio R, Contreras-Medina LM, Vazques-Barrios E, Mercado-Silva E, Torres-Pacheco I, Guevara-Gonzalez RG (2013) Application of neural networks to estimate carotenoid content during ripening in tomato fruits (Solanum lycopersicum). Scientia Horticulturae (162): 165-171.
• Williams III L, Martinson TE (2003) Nondestructive leaf area estimation of ‘Niagara’ and ‘DeChaunac’ grapevines. Scientia Horticulturae 98: 493-498.
• Yavuz S, Deveci M (2012) The effect of statistical normalization techniques on artificial neural network performance. Journal of Erciyes University, Faculty of Economics and Administrative Sciences 40: 167-187.
• Yuan H, Yang G, Li C, Wang Y, Liu J, Yu H, Feng H, Xu B, Zhao X, Yang X (2017) Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models. doi:10.3390/rs9040309.