Hande KÜÇÜKÖNDER, Adil AKYÜZ, Sedat BOYACI

A modeling study with an artificial neural network: developing estimation models for the tomato plant leaf area

The leaf area measurement is an important parameter in understanding the growth and physiology of a plant. Therefore, this study aimed to develop the best leaf area estimation model for tomato plants grown in plastic greenhouse conditions. The artificial neural network (ANN) and regression analysis techniques were used in the formation of a leaf area estimation model by using the leaf width and leaf length measurements determined by the linear measurement method. The plant material for the study consisted of 420 leaf samples of the Typhoon F1 tomato type grown in plastic greenhouse conditions. In the comparison of the created models according to both methods, the criteria of selecting low values for the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE), and high value for the determination coefficient (R2 ) were taken into account, and the best estimation models were determined. In the comparison made according to these criteria, it was concluded that the error values of the ANN model [R2= 0.96, RMSE = 3.30, MAE = 1.94, and MAPE = 0.05] were lower than those of the regression model [R2= 0.92, RMSE = 4.71, MAE = 3.31, and MAPE = 0.08], and that the ANN method provided a better fit to the actual values; therefore, the ANN model can be used as an alternative method in estimating the leaf area.

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Aase JK (1978). Relationship between leaf area and dry matter in winter wheat. Agron J 70: 563565.
Akkaya G (2007). Artificial neural networks and their applications in agriculture. Atatürk Üniversitesi Ziraat Fakültesi Dergisi 38: 195202 (article in Turkish with an abstract in English).
Astegiano ED, Favaro JC, Bouzo CA (2001). Estimation of leaf area in different cultivars of tomato (Lycopersicon esculentum Mill.). Invest Agr: Prod Prot Veg 16: 249256.
Beyhan MA, Uzun S, Kandemir D, Ozer H, Demirsoy M (2008). A model for predicting leaf area in young and old leaves of greenhouse type tomato (Lycopersicon esculentum, Mill.) by linear measurements. Journal of the Faculty of Agriculture OMU 23: 154157.
Blanco FF, Folegatti MV (2003). A new method for estimating the leaf area index of cucumber and tomato plants. Hortic Bras 21: 666669.
Blanco FF, Folegatti MV (2005). Estimation of leaf area for greenhouse cucumber by linear measurements under salinity and grafting. Sci Agric 62: 305309.
Caglar N (2009). Neural network based approach for determining the shear strength of circular reinforced concrete columns. Const Buil Mater 23: 32253232.
Campostrini E, Yamanishi OK (2001). Estimation of papaya leaf area using the central vein length. Sci Agric 58: 3942.
Canakci M, Hosoz M (2006). Energy and exergy analyses of a diesel engine fuelled with various biodiesels. Energ Source Part B 1: 379394.
Celik H, Odabas MS, Odabas F (2011). Leaf area prediction models for highbush blueberries (Vaccinium corymbosum L.) from linear measurements. Adv Food Sci 33: 1621.
Celik H, Uzun S (2002). Validation of leaf area estimation models (UZCELIK-I) evaluated for some horticultural plants. Pak J Bot 34: 4146.
Centritto ML, Massacci F, Pietrini A, Villani FMC, Zacchine M (2000). Improved growth and water use efficiency of cherry saplings under reduced light intensity. Ecol Res 15: 385392.
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. Sci Hortic 111: 330334.
Cristofori VR, Gyves YEM, Bignami C (2007). A simple model for estimating leaf area of hazelnut from linear measurements. Sci Hortic 113: 221225.
Demirsoy H (2009). Leaf area estimation in some species of fruit tree by using models as a non-destructive method. Fruits 64: 4551.
Demirsoy H, Demirsoy L (2003). A validated leaf area prediction model for some cherry cultivars in Turkey. Pak J Bot 35: 361 367.
Demirsoy H, Demirsoy L, Uzun S, Ersoy B (2004). Non-destructive leaf area estimation in peach. Europ J Hort Sci 69: 144146.
Demirsoy, H, Lang, GA (2010). Short communication. Validation of a leaf area estimation model for sweet cherry. Span J Agric Res 8: 830832.
Dumas Y (1990). Interrelation of linear measurements and leaf area or dry matter production in young tomato plants. HortScience 4: 172176.
Elizondo DA, McClendon RW, Hoongenboom G (1994). Neural network models for predicting flowering and physiological maturity of soybean. Transactions of the ASABE 37: 981988.
Elsner EA, Jubb GL (1988). Leaf area estimation of Concord grape leaves from simple linear measurements. Am J Enol Viticult 39: 9597.
Fallovo C, Cristofori V, Mendoza-De Gyves E, Rivera CM, Rea R, Fanasca S, Bignami C, Sassine Y, Rouphael Y (2008). Leaf area estimation model for small fruits from linear measurements. HortScience 43: 22632267.
Fausett LV (1994). Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Englewood Cliffs, NJ, USA: Prentice-Hall.
Gamiely SR, Mills WMHA, Smittle DA (1991). A rapid and nondestructive method for estimating leaf area of onions. HortScience 26: 206.
Guine RPF, Barroca MJ, Goncalves FJ, Alves M, Oliveira S, Mendes M (2015). Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. Food Chem 168: 454459.
Guzelbey IH, Cevik A, Gögüş MT (2006). Prediction of rotation capacity of wide flange beams using neural networks. J Const Steel Res 62: 950961.
Kersteins G, Hawes CW (1994). Response of growth and carbon allocation to elevated CO2 in young cherry (Prunus avium L.) saplings in relation to root environment. New Phytol 128: 607614.
Khoshnevisan B, Rafiee S, Omid M, Mousazadeh H, Rajaeifar MA (2014). Application of artificial neural networks for prediction of output energy and GHG emissions in potato production in Iran. Agr Syst 123: 120127.
Kumar R (2009). Calibration and validation of regression model for non-destructive leaf area estimation of saffron (Crocus sativus L.). Sci Hortic 122: 142145.
Lewis CD (1982). Industrial and Business Forecasting Methods. London, UK: Butterworths Publishing.
Liu M, Liu X, Li M, Fang M, Chi W (2010). Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices. Biosyst Eng 106: 223233.
Mamay M, Yanık E (2012). Determination of adult population development of Tomato leafminer [Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae)] in tomato growing areas in Şanlıurfa. Türk Entomol Bült 2: 189198.
Öztemel E (2003). Artificial Neural Networks. 2nd ed. İstanbul, Turkey: Papatya Publishing.
Öztürk S (2012). An analysis on the application of statistical regression methods for different data sets. GUSTIJ 2: 55-67.
Pala M, Caglar NA (2007). Parametric study for distortional buckling stress on cold-formed steel using a neural network. J Const Steel Res 63: 686691.
Parmar RS, McClendon RW, Hoogenboom G, Blankenship PD, Cole RJ, Dorner JW (1997). Estimation of aflatoxin contamination in preharvest peanuts using neural networks. Transactions of the ASAE 40: 809813.
Peksen E (2007). Non-destructive leaf area estimation model for faba bean (Vicia faba L.). Sci Hortic 113: 322328.
Rajendran PC, Thamburaj S (1987). Estimation of leaf area in watermelon by linear measurements. South Indian Hort 35: 325327.
Rivera CM, Rouphael Y, Cardarelli M, Colla G (2007). A simple and accurate equation for estimating individual leaf area of eggplant from linear measurements. Europ J Hort Sci 70: 228230.
Robins NS, Pharr DM (1987). Leaf area prediction models for cucumber from linear measurements. Hort Sci 22: 12641266.
Schwarz D, Kläring HS (2001). Allometry to estimate leaf area of tomato. J Plant Nutr 24: 12911309.
Serdar Ü, Demirsoy H (2006). Non-destructive leaf area estimation in chestnut. Sci Hortic 108: 227230.
Shahin M, Elchanakani M (2008). Neural networks for ultimate pure bending of steel circular tubes. J Const Steel Res 64: 624633.
Smart RE (1985). Principles of grapevine canopy microclimate manipulation with implications for yield and quality: a review. Am J Enol Vitic 36: 230239.
Smith GN (1986) Probability and Statistics in Civil Engineering: an Introduction. London, UK: Collins.
Tadesse Z, Patel KA, Chaudhary S, Nagpal AK (2012). Neural networks for prediction of deflection in composite bridges. J Const Steel Res 68: 138149.
Takma Ç, Atıl H, Aksakal V (2012). Comparison of multiple linear regression and artificial neural network models goodness of fit to lactation milk yields. Kafkas Univ Vet Fak Derg 18: 941944.
Tamari S, Wösten JHM, Ruiz-Suárez C (1996). Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Sci Soc Am J 60: 17321741.
TÜİK (2013). Örtü altı sebze ve meyve üretimi. Türkiye İstatistik Kurumu (in Turkish).
Uzun S, Çelik H (1999). Leaf area prediction models (uzçelik-1) for different horticultural plants. Turk J Agric For 23: 645650.
Vazquez-Cruz MA, Jimenez-Garcia SN, Luna-Rubio R, ContrerasMedina LM, Vazquez-Barrios E, Mercado-Silva E, TorresPacheco I, Guevara-Gonzalez RG (2013). Application of neural networks to estimate carotenoid content during ripening in tomato fruits (Solanum lycopersicum). Sci Hortic 162: 165171.
Vazquez-Cruz MA, Luna-Rubio R, Contreras-Medina LM, TorresPacheco I, Guevara-Gonzalez RG (2012). Estimating the response of tomato (Solanum lycopersicum) leaf area to changes in climate and salicylic acid applications by means of artificial neural networks. Biosyst Eng 112: 319327.
Were K, Tien BD, Dick B, Singh BR (2015). A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol Indic 52: 394 403.
Williams LE (1987). Growth of Thompson Seedless grapevines. I. Leaf area development and dry weight distribution. J Am Soc Hort Sci 112: 325330.