designed for dynamic nonlinear systems (Rahimikhoob 2010). ANNs are now used
in many computer-based applications where there is a need to identify patterns or
‘‘learn’’ relationships between a set of input variables and a set of output variables
(Danson and Rowland 2003). During the last decade, there has been a significant
increase in agronomic ANN applications (Huang et al. 2010) including crop
development modeling, crop yield prediction, evapotranspiration estimations, soil
water, and salt content assessments (Dai et al. 2011; Fortin et al. 2010; Liu et al.
2009). Another important variable, which is commonly used in modeling crop, is
leaf area. There have been a few attempts to produce leaf area estimation models
predicting leaf area by means of using simple linear measurements like length and
width (Beyhan et al. 2008). Vazquez-Cruz et al. (2012) developed an ANN model
to determine the response of tomato leaf area to different climate conditions such
as CO 2 concentration, PAR, and temperature along with different salicylic acid
treatments. The results showed that ANN model was a useful tool in research and
understanding the complex relationships between greenhouse conditions and leaf
area development.
More research is needed in order to increase performance of training algorithms
to improve the ability of neural systems to learn from climatic and physiological
data patterns for crop growth prediction, and the forecasting performance to
provide a useful guidance or reference for yield estimation.
References
Abdel-Razik M (1989) A model of the productivity of olive trees under optimal water and
nutrient supply in desert conditions. Ecol Modelling 45:179–204
André MJ (2013) Modelling 18O2 and 16O2 unidirectional fluxes in plants: IV. Role of
conductance and laws of its regulation in C3 plants. J Biosyst 113:104–114 http://dx.doi.org/
10.1016/j.biosystems.2012.11.002
Auffhammer M, Ramanathan V, Vincent JR (2006) Integrated model shows that atmospheric
brown clouds and greenhouse gases have reduced rice harvests in India. Proc Natl Acad Sci
USA 103:19668–19672
Baumgaertner J, Graf B, Zahner P (1984) A stochastic population model to simulate the annual
growth pattern of mature Golden Delicious apple trees. Recherche Agronomique Suisse
23:489–501
Benes M, Feiresl E (2008) Topics in mathematical modeling. Jindrich Necas center for
mathematical modeling, Lecture notes, Volume 4. Matfyz Press, Prague
Bender EA (1978) An introduction to mathematical modelling. Wiley, New York
Bernacchi C, Rosenthal D, Pimentel C, Long S, Farquhar G (2009) Modeling the temperature
dependence of C3 photosynthesis. In: Laisk A, Nedbal L, Govindjee L (eds) Photosynthesis in
silico: understanding complexity from molecules to ecosystems. Springer, Dordrecht,
pp 231–246
Bertin N, Bussières P, Génard M (2006) Ecophysiological models of fruit quality: a challenge for
peach and tomato. Acta Horticulturae 718:633–645
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 (Lycopersiconesculentum Mill.) by linear
measurements. J Agric Fac Ondokuz Mayis 23:154–157
72
M. A. Vázquez-Cruz et al.
in many computer-based applications where there is a need to identify patterns or
‘‘learn’’ relationships between a set of input variables and a set of output variables
(Danson and Rowland 2003). During the last decade, there has been a significant
increase in agronomic ANN applications (Huang et al. 2010) including crop
development modeling, crop yield prediction, evapotranspiration estimations, soil
water, and salt content assessments (Dai et al. 2011; Fortin et al. 2010; Liu et al.
2009). Another important variable, which is commonly used in modeling crop, is
leaf area. There have been a few attempts to produce leaf area estimation models
predicting leaf area by means of using simple linear measurements like length and
width (Beyhan et al. 2008). Vazquez-Cruz et al. (2012) developed an ANN model
to determine the response of tomato leaf area to different climate conditions such
as CO 2 concentration, PAR, and temperature along with different salicylic acid
treatments. The results showed that ANN model was a useful tool in research and
understanding the complex relationships between greenhouse conditions and leaf
area development.
More research is needed in order to increase performance of training algorithms
to improve the ability of neural systems to learn from climatic and physiological
data patterns for crop growth prediction, and the forecasting performance to
provide a useful guidance or reference for yield estimation.
References
Abdel-Razik M (1989) A model of the productivity of olive trees under optimal water and
nutrient supply in desert conditions. Ecol Modelling 45:179–204
André MJ (2013) Modelling 18O2 and 16O2 unidirectional fluxes in plants: IV. Role of
conductance and laws of its regulation in C3 plants. J Biosyst 113:104–114 http://dx.doi.org/
10.1016/j.biosystems.2012.11.002
Auffhammer M, Ramanathan V, Vincent JR (2006) Integrated model shows that atmospheric
brown clouds and greenhouse gases have reduced rice harvests in India. Proc Natl Acad Sci
USA 103:19668–19672
Baumgaertner J, Graf B, Zahner P (1984) A stochastic population model to simulate the annual
growth pattern of mature Golden Delicious apple trees. Recherche Agronomique Suisse
23:489–501
Benes M, Feiresl E (2008) Topics in mathematical modeling. Jindrich Necas center for
mathematical modeling, Lecture notes, Volume 4. Matfyz Press, Prague
Bender EA (1978) An introduction to mathematical modelling. Wiley, New York
Bernacchi C, Rosenthal D, Pimentel C, Long S, Farquhar G (2009) Modeling the temperature
dependence of C3 photosynthesis. In: Laisk A, Nedbal L, Govindjee L (eds) Photosynthesis in
silico: understanding complexity from molecules to ecosystems. Springer, Dordrecht,
pp 231–246
Bertin N, Bussières P, Génard M (2006) Ecophysiological models of fruit quality: a challenge for
peach and tomato. Acta Horticulturae 718:633–645
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 (Lycopersiconesculentum Mill.) by linear
measurements. J Agric Fac Ondokuz Mayis 23:154–157
72
M. A. Vázquez-Cruz et al.