Estimating the response of tomato (Solanum lycopersicum) leaf area to changes in climate and salicylic acid applications by means of artificial neural networks

Vazquez-Cruz, Moises Alejandro; Luna-Rubio, R.; Contreras-Medina, Luis Miguel; Torres-Pacheco, Irineo; Guevara-Gonzalez, R.G.

doi:10.1016/j.biosystemseng.2012.05.003

Cited by 25 publications

(18 citation statements)

References 32 publications

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“…The ANN method was more successful in estimating the actual values according to regression analysis ( Figure 6). Similar results were reported in many ANN studies in the field of agriculture (Liu et al, 2010;Vazquez-Cruz et al, 2012;Khoshnevisan et al, 2014;Guine, 2015;Were et al, 2015).…”

Section: Discussionsupporting

confidence: 90%

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

Küçükönder¹,

Boyaci²,

Akyüz³

2016

Turk J Agric For

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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 (R 2) 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 [R 2 = 0.96, RMSE = 3.30, MAE = 1.94, and MAPE = 0.05] were lower than those of the regression model [R 2 = 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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Section: Discussionsupporting

confidence: 90%

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

Küçükönder¹,

Boyaci²,

Akyüz³

2016

Turk J Agric For

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“…As ANN and ANFIS models use the relationships between input and output values, they explained greater variability and had a higher capacity to estimate the leaf area than linear regression models. The ANN and ANFIS models also contribute to understanding the relationship between leaf area development and various climatic factors (Vazquez‐Cruz et al., 2012).…”

Section: Resultsmentioning

confidence: 99%

Image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro‐fuzzy inference system, and regression methods

et al. 2021

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In this study, the leaf area was estimated by different regression models and new methods of image processing by using artificial intelligence (AI) in bread (Triticum aestivum L.) and durum wheat (Triticum durum L.) and triticale (×Tritosecale Wittm. ex A. Camus) at seedling, booting, and milk development stages. Data on leaf traits in 1,000 plants of breed wheat, triticale, and durum wheat were studied. Among regression models using general data, LA = + √ , LA = + (), and LA = + (∕) models had a R 2 > 90% for all three cultivars (L and W represented length and width of leaves). For wheat bread, models + () and √ × () exhibited a good estimate of the leaf area at all stages of growth, whereas in triticale, the linear models that used the product of √ × and √ were more suitable to estimate leaf areas in booting and milk development. The multilayer perceptron (MLP) neural network modeling indicated that the 'trainlm,' 'trainlm,' and 'traincgb' algorithms with an optimal structure of 2-10-1, 2-3-1, and 2-10-1 had the least amount of the mean square error for bread wheat, triticale, and durum wheat, respectively, for each of the growth stages. Furthermore, based on the adaptive neuro-fuzzy inference system (ANFIS) method, a very accurate estimate of the leaf area was obtained for each of the growth stages. The comparison of different models in the leaf area estimation showed that unlike estimating leaf area by regression, AI-based methods did not depend on plant growth stages. In this study, we suggested the utilization of image processing and artificial intelligence in leaf area estimation. It will accelerate leaf area measurement in the field, by extending these methods and transferring it into smartphone applications.

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“…The layout of the most utilized artificial neural network is the multilayer perceptron. Among the several optimisation techniques that can be directly applied in the multilayerperceptron architecture, back-propagation is the most popular algorithm used for its training (Cai et al, 2010;Gautam et al, 2011;Leduc et al, 2001;Vazquez-Cruz et al, 2012). This algorithm has been thoroughly described (Fausett, 1994;Haykin, 1994;Patterson, 1996;Rumelhart, Hinton, & Williams, 1986).…”

Section: Artificial Neural Network Modellingmentioning

confidence: 97%

Estimation of Weibull function parameters for modelling tree diameter distribution using least squares and artificial neural networks methods

Diamantopoulou

Özçelık

Crecente-Campo³

et al. 2015

Biosystems Engineering

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Estimating the response of tomato (Solanum lycopersicum) leaf area to changes in climate and salicylic acid applications by means of artificial neural networks

Cited by 25 publications

References 32 publications

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

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

Image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro‐fuzzy inference system, and regression methods

Estimation of Weibull function parameters for modelling tree diameter distribution using least squares and artificial neural networks methods

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