Hashimoto Y (1997) Applications of artificial neural networks and genetic algorithms to
agricultural systems. Comput Electron Agric 18:71–72
Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, Cambridge
Hertog MLATM, Tijskens LMM (1998) Modelling modified atmosphere packaging of perishable
produce: keeping quality at dynamic conditions. Acta Alimentaria 27:53–62
Hertog MLATM, Lammertyn J, Desmet M, Scheerlinck N, Nicolaï BM (2004) The impact of
biological variation on postharvest behaviour of tomato fruit. Postharvest Biol Technol
34(3):271–284
Heuvelink E, Bertin N (1994) Dry matter partitioning in a tomato crop: comparison of two
simulation models. J Hortic Sci 69:885–903
Hockaday S, Beddow TA, Stone M, Hancock P, Ross LG (2000) Using truss networks to estimate
the biomass of Oreochromisniloticus, and to investigate shape characteristics. J Fish Biol
57:981–1000
Hoffmann J, Ellingwood C, Bonsu O, Bentil D (2004) Ecological model selection via
evolutionary computation and information theory. Genet Program Evolvable Mach 5:229–241
Huang Y, Lan Y, Thomson SJ, Fang A, Hoffman WC, Lacey RE (2010) Development of soft
computing and applications in agricultural and biological engineering. Comput Electron Agric
71:107–127
Kita E (2011) Evolutionary algorithms. Intech, Rijeka
Krenker A, Bester J, Kos A (2011) Introduction to the artificial neural networks. In: Kenji S (Ed.)
Artificial neural networks: methodological advances and biomedical applications. InTech,
Rijeka. ISBN: 978-953-307-243-2
Kumar JLG, Zhao YQ (2011) A review on numerous modeling approaches for effective,
economical and ecological treatment wetlands. J Environ Manage 92:400–406
Léchaudel M, Génard M, Lescourret F, Urban L, Jannoyer M (2005) Modelling effects of weather
and source-sink relationships on mango fruit growth. Tree Physiol 25:583–597
Lescourret F, Ben-Mimoun M, Génard M (1998) A simulation model of growth at the shootbearing fruit level. I. Description and parameterization for peach. Eur J Agron 9:173–188
Lescourret F, Génard M (2005) A virtual peach fruit model simulating changes in fruit quality
during the final stage of fruit growth. Tree Physiol 25:1303–1315
Li W, Wang K, Su H (2011) Optimal harvesting policy for stochastic logistic population model.
Appl Math Comput 218:157–162
Lines JA, Tillet RD, Ross LG, Chan D, Hockaday S, McFarlane NJB (2001) An automatic imagebased system for estimating the mass offree-swimming fish. Comput Electron Agric 31:151–168
Liu X, Kang S, Li F (2009) Simulation of artificial neural network model for trunk sap flow of
Pyruspyrifolia and its comparison with multiple linear regression. Agric Water Manage
96:939–945
Ljung L, Gland T (1994) Modeling of dynamic systems. Prentice Hall, Englewood
Lobit P, Génard M, Wu BH, Soing P, Habib R (2003) Modelling citrate metabolism in fruits:
responses to growth and temperature. J Exp Bot 54:2489–2501
Lombardozzi D, Sparks J, Bonan G, Levis S (2012) Ozone exposure causes a decoupling of
conductance and photosynthesis: implications for the ball-berry stomatal conductance model.
Oecologia 169:651–659
Millan-Almaraz J, Guevara-Gonzalez R, de JesusRomero-Troncoso R, Osornio-Rios R, TorresPacheco I (2009) Advantages and disadvantages on photosynthesis measurement techniques:
a review. Afr J Biotechnol 8:7340–7349
Minsky ML, Papert SA (1969) Perceptrons. MIT Press, Cambridge. Expanded Edition 1990
Miwa T (1986a) Mathematical model making in problem solving—Japanese pupils’ performance
and awareness of assumptions. In: Becker J and Miwa T (eds) Proceedings of the U.S.-Japan
seminar on mathematical problem solving. East-West Centre Honolulu, pp 401–417
Miwa T (1986b) Mathematical modeling and development of teaching materials. In: Miwa T (ed)
Development of teaching materials of mathematical modeling in school mathematics (in
Japanese). Reports for scientific grant by Japan society for the promotion of science, pp 22–28
74
M. A. Vázquez-Cruz et al.
agricultural systems. Comput Electron Agric 18:71–72
Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, Cambridge
Hertog MLATM, Tijskens LMM (1998) Modelling modified atmosphere packaging of perishable
produce: keeping quality at dynamic conditions. Acta Alimentaria 27:53–62
Hertog MLATM, Lammertyn J, Desmet M, Scheerlinck N, Nicolaï BM (2004) The impact of
biological variation on postharvest behaviour of tomato fruit. Postharvest Biol Technol
34(3):271–284
Heuvelink E, Bertin N (1994) Dry matter partitioning in a tomato crop: comparison of two
simulation models. J Hortic Sci 69:885–903
Hockaday S, Beddow TA, Stone M, Hancock P, Ross LG (2000) Using truss networks to estimate
the biomass of Oreochromisniloticus, and to investigate shape characteristics. J Fish Biol
57:981–1000
Hoffmann J, Ellingwood C, Bonsu O, Bentil D (2004) Ecological model selection via
evolutionary computation and information theory. Genet Program Evolvable Mach 5:229–241
Huang Y, Lan Y, Thomson SJ, Fang A, Hoffman WC, Lacey RE (2010) Development of soft
computing and applications in agricultural and biological engineering. Comput Electron Agric
71:107–127
Kita E (2011) Evolutionary algorithms. Intech, Rijeka
Krenker A, Bester J, Kos A (2011) Introduction to the artificial neural networks. In: Kenji S (Ed.)
Artificial neural networks: methodological advances and biomedical applications. InTech,
Rijeka. ISBN: 978-953-307-243-2
Kumar JLG, Zhao YQ (2011) A review on numerous modeling approaches for effective,
economical and ecological treatment wetlands. J Environ Manage 92:400–406
Léchaudel M, Génard M, Lescourret F, Urban L, Jannoyer M (2005) Modelling effects of weather
and source-sink relationships on mango fruit growth. Tree Physiol 25:583–597
Lescourret F, Ben-Mimoun M, Génard M (1998) A simulation model of growth at the shootbearing fruit level. I. Description and parameterization for peach. Eur J Agron 9:173–188
Lescourret F, Génard M (2005) A virtual peach fruit model simulating changes in fruit quality
during the final stage of fruit growth. Tree Physiol 25:1303–1315
Li W, Wang K, Su H (2011) Optimal harvesting policy for stochastic logistic population model.
Appl Math Comput 218:157–162
Lines JA, Tillet RD, Ross LG, Chan D, Hockaday S, McFarlane NJB (2001) An automatic imagebased system for estimating the mass offree-swimming fish. Comput Electron Agric 31:151–168
Liu X, Kang S, Li F (2009) Simulation of artificial neural network model for trunk sap flow of
Pyruspyrifolia and its comparison with multiple linear regression. Agric Water Manage
96:939–945
Ljung L, Gland T (1994) Modeling of dynamic systems. Prentice Hall, Englewood
Lobit P, Génard M, Wu BH, Soing P, Habib R (2003) Modelling citrate metabolism in fruits:
responses to growth and temperature. J Exp Bot 54:2489–2501
Lombardozzi D, Sparks J, Bonan G, Levis S (2012) Ozone exposure causes a decoupling of
conductance and photosynthesis: implications for the ball-berry stomatal conductance model.
Oecologia 169:651–659
Millan-Almaraz J, Guevara-Gonzalez R, de JesusRomero-Troncoso R, Osornio-Rios R, TorresPacheco I (2009) Advantages and disadvantages on photosynthesis measurement techniques:
a review. Afr J Biotechnol 8:7340–7349
Minsky ML, Papert SA (1969) Perceptrons. MIT Press, Cambridge. Expanded Edition 1990
Miwa T (1986a) Mathematical model making in problem solving—Japanese pupils’ performance
and awareness of assumptions. In: Becker J and Miwa T (eds) Proceedings of the U.S.-Japan
seminar on mathematical problem solving. East-West Centre Honolulu, pp 401–417
Miwa T (1986b) Mathematical modeling and development of teaching materials. In: Miwa T (ed)
Development of teaching materials of mathematical modeling in school mathematics (in
Japanese). Reports for scientific grant by Japan society for the promotion of science, pp 22–28
74
M. A. Vázquez-Cruz et al.