Greenhouses within the Agricultura 4.0 interface

Edilson Costa, Murilo Battistuzzi Martins, Eduardo Pradi Vendruscolo, Abimael Gomes da Silva, Tiago Zoz, Flávio Ferreira da Silva Binotti, Travis Wilson Witt, Cássio de Castro Seron

Resumo


Global technological advances can be applied to all production sectors to improve people’s daily lives, efficiently deliver information, and product safety. This study is a literature review of the use of the Agricultura 4.0 interface in greenhouses and the improvements that these technologies have made to the industry. For the agricultural sector, especially intensive plant production in protected environments, Agricultura 4.0 technologies are widely used to reduce human error and ensure high quality plant products. Mathematical modeling, computer software, electronic meters, robotics, intelligent real-time system decisions, and automatic activity control throughout the production cycle guarantees extreme production safety in protected cultivation systems and precision planting environments. The accuracy, precision, and performance of Agricultura 4.0 in greenhouses depends, as in others agricultural sectors, on improved communication between digital platforms as well as in stable Internet for machine programming and operation in the production systems.

Palavras-chave


Robotics; Real time; Intensive production; Information technology

Texto completo:

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Referências


ADEYEMI, O.; GROVE, I.; PEETS, S.; NORTON, T. Advanced monitoring and management systems for improving sustainability in precision irrigation. Sustainability. v. 9, n. 353, p. 1-29, 2017. DOI: http://doi.org/10.3390/su9030353; Disponível em: . Acesso em: 20 ago. 2020.

AHAMED, M. S.; GUO, H.; TANINO, K. A quasi-steady state model for predicting the heating requirements of conventional greenhouses in cold regions. Information Processing in Agriculture, v. 5, n. 1, p. 33-46, 2018. DOI: http://doi.org/10.1016/j.inpa.2017.12.003; Disponível em: . Acesso em: 20 ago. 2020.

ALHNAITY, B.; PEARSON, S.; LEONTIDIS, G.; KOLLIAS, S. Using deep learning to predict plant growth and yield in greenhouse environments. arXiv, 2019, arXiv:1907.00624. Disponível em: < http://arxiv.org/abs/1907.00624>. Acesso em: 20 ago. 2020.

ARAD. B.; BALENDONCK, J.; BARTH, R.; BEN-SHAHAR, O.; EDAN, Y.; HELLSTRÖM, T.; HEMMING, J.; KURTSER, P.; RINGDAHL, O.; TIELEN, T.; TUIJL, B. V. Development of a sweet pepper harvesting robot. Journal of Field Robotics, v. 37, n.6, p. 1027-1039, 2020. DOI: http://doi.org/10.1002/rob.21937; Disponível em: . Acesso em: 12 set. 2020.

ARCHANA, O.; PRIYA, R. Design and implementation of automatic plant watering system. International Journal of Advanced Engineering and Global Technology, v. 4, n. 1, 2016.

ARDIANSAH, I.; BAFDAL, N.; SURYADI, E.; BONO, A. Greenhouse monitoring and automation using Arduino: a review on precision farming and internet of things (IoT). International Journal on Advanced Science, Engineering and Information Technology, v. 10, n. 2, p. 703-709, 2020. Disponível em: . Acesso em: 12 set. 2020.

BAJER, L.; KREJCAR, O. Design and realization of low cost control for greenhouse environment with remote control. Internacional Federation of automatic control- IFAC, v. 48, n. 4, p. 368-373, 2015. DOI: http://doi.org/10.1016/j.ifacol.2015.07.062; Disponível em: < https://www.sciencedirect.com/science/article/pii/S240589631500837X?via%3Dihub>. Acesso em: 12 set. 2020.

BECHAR, A.; VIGNEAULT, C. Agricultural robots for field operations: concepts and components. Biosystems Engineering, v. 149, p. 94-111, 2016. DOI: https://doi.org/10.1016/j.biosystemseng.2016.06.014; Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S1537511015301914>. Acesso em: 29 set. 2020.

BELFORTE, G.; DEBOLI, R.; GAY, P.; PICCAROLO, P.; RICAUDA AIMONINO, D. Robot design and testing for greenhouse applications. Biosystems Engineering, v. 95, n. 3, p. 309-321, 2006. DOI: http://doi.org/10.1016/j.biosystemseng.2006.07.004; Disponível em: . Acesso em: 20 ago. 2020.

CHAVAN, C. H.; KARNADE V. Wireless monitoring of soil moisture, temperature and humidity using ZigBee in agriculture. International Journal of Engineering Trends and Technology, v. 11, n. 10, p. 493-497, 2014. Disponível em: . Acesso em: 29 set. 2020.

CHEN, J.; XU, F.; TAN, D.; SHEN, Z.; ZHANG, L.; AI, Q. A control method for agricultural greenhouses heating based on computational fluid dynamics and energy prediction model. Applied Energy, v. 141, n. 1, p. 106-118, 2015. DOI: http://doi.org/10.1016/j.apenergy.2014.12.026; Disponível em: . Acesso em: 29 set. 2020.

CHIU, Y. C.; YANG, P. Y.; CHEN, S. Development of the endeffector of a picking robot for greenhouse-grown tomatoes. Applied Engineering in Agriculture, v. 29, n. 6, p. 1001-1009, 2013. DOI: 10.13031/aea.29.9913 Disponível em: . Acesso em: 12 set. 2020.

COSTA, E.; LEAL, P. A. M.; CARMO JUNIOR, R. R. Modelo de simulação da temperatura e umidade relativa do ar no interior de estufa plástica. Engenharia Agrícola, Jaboticabal, v. 24, n. 1, p. 57-67, 2004. DOI: http:// doi.org/10.1590/S0100-69162004000100008; Disponível em: . Acesso em: 20 ago. 2020.

DABACH, S.; LAZAROVITCH, N.; ŠIMU˚NEK, J.; SHANI, U. Numerical investigation of irrigation scheduling based on soil water status. Irrigation Science, v. 31, p. 27-36, 2013. DOI: http://doi.org/10.1007/s00271-014-0439-z; Disponível em: . Acesso em: 15 set. 2020.

DELGODA, D.; MALANO, H.; SALEEM, S. K.; HALGAMUGE, M. N. Irrigation control based on model predictive control (MPC): formulation of theory and validation using weather forecast data and AQUACROP model. Environmental Modelling & Software. v. 78, p. 40-53, 2016. DOI: https://doi.org/10.1016/j.envsoft.2015.12.012 Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S1364815215301262. Acesso em: 25 set. 2020.

DOGTOOTH. http://www.dogtooth.tech/

EHRET, D. L.; HILL, B. D.; HELMER, T.; EDWARDS, D. R. Neural network modeling of greenhouse tomato yield, growth and water use from automated crop monitoring data. Computers and Electronics in Agriculture, v. 79, n. 1, p. 82-89, 2011. DOI: https://doi.org/10.1016/j.compag.2011.07.013; Disponível em: . Acesso em: 12 set. 2020.

ELVANIDI, A.; KATSOULAS, N.; KITTAS, C. Automation for water and nitrogen deficit stress detection in soilless tomato crops based on spectral indices. Horticulturae, v. 4, n. 4, 47, 2018. DOI: http://doi.org/10.3390/horticulturae4040047; Disponível em: . Acesso em: 12 set. 2020.

ESCAMILLA-GARCÍA, A.; SOTO-ZARAZÚA, G. M.; TOLEDANO-AYALA, M.; RIVAS-ARAIZA, E.; GASTÉLUM-BARRIOS, A. Applications of artificial neural networks in greenhouse technology and overview for smart agriculture development. Applied Sciences, v. 10, n. 11, 2020. DOI: http://doi.org/10.3390/app10113835; Disponível em: . Acesso em: 12 set. 2020.

FOGLIA, M.; GENTILE, A.; REINA, G. (2008) Robotics for agricultural systems. In: BILLINGSLEY, J.; BRADBEER, R. (Eds) Mechatronics and machine vision in practice. Berlin, Heidelberg: Springer, 2008. DOI: http://doi.org/10.1007/978-3-540-74027-8_27; Disponível em: . Acesso em: 29 set. 2020.

FUE, K. G.; PORTER, W. M.; BARNES, E. M.; RAINS, G. C. An extensive review of mobile agricultural robotics for field operations: focus on cotton harvesting. AgriEngineering, v. 2, n. 1, p. 150-174, 2020. DOI: http://doi.org/10.3390/agriengineering2010010; Disponível em: . Acesso em: 20 ago. 2020.

GAT, G.; GAN-MOR, S.; DEGANI, A. Stable and robust vehicle steering control using an overhead guide in greenhouse tasks. Computers and Electronics in Agriculture, v. 121, p. 234-244, 2016. DOI: http://doi.org/10.1016/j.compag.2015.12.019; Disponível em: < https://www.sciencedirect.com/science/article/abs/pii/S0168169915004019>. Acesso em: 20 ago. 2020.

GIUSTI, E.; MARSILI-LIBELLI, S. A fuzzy decision support system for irrigation and water conservation in agriculture. Environmental Modelling & Software, v. 63, p. 73-86, 2015. DOI: http://doi.org/10.1016/j.envsoft.2014.09.020; Disponível em: . Acesso em: 20 ago. 2020.

GUPTA, M. K.; CHANDRA, P.; SAMUEL, D. V. K.; SINGH, B.; SINGH, A.; GARG, M. K. Modeling of tomato seedling growth in greenhouse. Agricultural Research, v. 1, p. 362–369, 2012. DOI: http://doi.org/10.1007/s40003-012-0035-5; Disponível em: . Acesso em: 29 set. 2020.

HARDAHA, M. K.; CHOUHAN, S. S.; AMBAST, S. K. Application of artificial neural network in prediction of response of farmers water management decisions on wheat yield. Journal of Agricultural Engineering. v. 49, n. 3, p. 32-40, 2012. Disponível em: . Acesso em: 29 set. 2020.

HARISHANKAR, S.; KUMAR R. S.; SUDHARSAN K. P.; VIGNESH, U.; VIVEKNATH, T. Solar powered smart irrigation system. Advance in Electronic and Electric Engineering, v. 4, n. 4, p. 341-346, 2014. Disponível em: . Acesso em: 29 set. 2020.

HEMMING, J. Automation and robotics in the protected environment, current developments and challenges for the future. Bologna: 28th Club of Bologna Members’ Meeting, november 2018. Disponível em: . Acesso em: 29 set. 2020.

HERAVI, A.; AHMAD, D.; HAMEED, I. A.; SHAMSHIRI, R. R.; BALASUNDRAM, S. K.; YAMIN, M. Development of a field robot platform for mechanical weed control in greenhouse cultivation of cucumber. In: ZHOU, J.; ZHANG, B. (Ed.). Agricultural robots: fundamentals and applications, 2018. Cap. 2., p. 11-29. DOI: DOI: 10.5772/intechopen.80935. Disponível em: . Acesso em: 29 set. 2020.

HUANG, C. H.; HUANG, C. C.; HUANG, C. L. Application of intelligent energy saving in smart greenhouse farm with wireless technique. Advanced Science Letters, v. 19, n. 10, p. 2909-2913, 2013. DOI: http://doi.org/10.3390/app10113835; Disponível em: . Acesso em: 12 set. 2020.

JOLLIET, O. Hortitrans, a model for predicting and optimizing humidity and transpiration in greenhouses. Journal of Agricultural Engineering Research, v. 57, n. 1, p. 23-37, 1994. DOI: http://doi.org/10.1006/jaer.1994.1003; Disponível em: < https://www.sciencedirect.com/science/article/abs/pii/S0021863484710031?via%3Dihub>. Acesso em: 12 set. 2020.

KALOXYLOS, A.; EIGENMANN, R,; TEYE, F.; POLITOPOULOU, Z.; WOLFERT, S.; SHRANK, C.; DILLINGER, M.; LAMPROPOULOU, I.; ANTONIOU, E.; PESONEN, L.; NICOLE, H.; THOMAS, F.; ALONISTIOTI, N.; KORMENTZAS, G. Farm management systems and the Future Internet era. Computers and Electronics in Agriculture, v. 89, p. 130-144, 2012. DOI: http://doi.org/10.1016/j.compag.2012.09.002; Disponível em: . Acesso em: 29 set. 2020.

KANSARA, K.; ZAVERI, V.; SHAH, S.; DELWADKAR, S.; JANI, K. Sensor based automated irrigation system with IoT: a technical review. International Journal of Computer Science and Information Technologies, v. 6, n. 6, p.5331-5333, 2015. Disponível em: . Acesso em: 12 set. 2020.

KOTHARI, S.; PANWAR, N. L. Steady state thermal model for predicting micro-climate inside the greenhouse. Journal of the Institution of Engineers (India): Agricultural Engineering Division, v. 88, p. 52-55, 2007.

KPONYO, J. J.; OPARE, K. A. B.; RAHMAN, A. A.; AGYEMANG, J. O. An intelligent irrigation system for rural agriculture. International Journal of Applied Agricultural Sciences, v. 5, n. 3, p. 75-81, 2019. Disponível em: < http://www.sciencepublishinggroup.com/journal/paperinfo?journalid=343&doi=10.11648/j.ijaas.20190503.13>. Acesso em: 29 set. 2020.

KWEON, G. Delineation of site-specific productivity zones using soil properties and topographic attributes with a fuzzy logic system. Biosystems Engineering, v. 112, n. 4, p. 261-277, 2012. DOI: http://doi.org/10.1016/j.biosystemseng.2012.04.009; Disponível em: . Acesso em: 12 set. 2020.

LI, N.; XIAO, Y.; SHEN, L.; XU, Z.; LI, B.; YIN, C. Smart agriculture with an automated IoT-based greenhouse system for local communities. Advances in Internet of Things, v. 9, n. 2, p. 15-31, 2019. DOI: http://doi.org/10.4236/ait.2019.92002; Disponível em: < https://www.scirp.org/journal/paperinformation.aspx?paperid=92183>. Acesso em: 29 set. 2020.

LINKER, R.; SEGINER, I. Greenhouse temperature modeling: a comparison between sigmoid neural networks and hybrid models. Mathematics and Computers in Simulation, v. 65, n. 1-2, p. 19-29, 2004. DOI: http://doi.org/10.1016/j.matcom.2003.09.004; Disponível em: < https://www.sciencedirect.com/science/article/abs/pii/S037847540300137X>. Acesso em: 29 set. 2020.

LINKER, R.; SEGINER, I.; GUTMAN, P. O. Optimal CO2 control in a greenhouse modeled with neural networks. Computers and Electronics in Agriculture, v. 19, n. 3, p. 289-310, 1998. DOI: http://doi.org/10.1016/S0168-1699%2898%2900008-8; Disponível em: . Acesso em: 29 set. 2020.

LOZOYA, C.; MENDOZA, C.; MEJÍA, L.; QUINTANA, J.; MENDOZA, G.; BUSTILLOS, M.; ARRAS, O.; SOLÍS, L. Model predictive control for closed-loop irrigation. IFAC Proceedings Volumes. v. 47, n. 3 p. 4429-4434, 2014. DOI: http://doi.org/10.3182/20140824-6-ZA-1003.02067; Disponível em: . Acesso em: 12 set. 2020.

LU, T.; VILJANEN, M. Prediction of indoor temperature and relative humidity using neural network models: model comparison. Neural Computing and Applications, v. 15, p. 345-357, 2009. DOI: http://doi.org/10.1007/s00521-008-0185-3; Disponível em: . Acesso em: 29 set. 2020.

MAO, H.; HAN, L.; HU, J.; KUMI, F. Development of a pincette-type pick-up device for automatic transplanting of greenhouse seedlings. Applied Engineering in Agriculture, v. 30, n. 4, p. 547-556, 2014. DOI: http://dx.doi.org/10.13031/aea.30.10550; Disponível em: . Acesso em: 12 set. 2020.

MCCARTHY, A. C.; HANCOCK, N. H.; RAINE, S. R. Development and simulation of sensor-based irrigation control strategies for cotton using the VARIwise simulation framework. Computers and Electronics in Agriculture. v. 101, p. 148-162, 2014. DOI: https://doi.org/10.1016/j.compag.2013.12.014; Disponível em: . Acesso em: 12 set. 2020.

MEKKI, M.; ABDALLAH, O.; AMIN, M. B. M.; ELTAYEB, M.; TAFAOUL, A.; BABIKER, A. Greenhouse monitoring and control system based on wireless sensor network. International Eletronics and Embedded systems engineering-IEEE, P.384-387 2015. DOI: http://doi.org/10.1109/ICCNEEE.2015.7381396; Disponível em: < https://ieeexplore.ieee.org/document/7381396>. Acesso em: 12 set. 2020.

MENDES, W. R.; ARAÚJO, F. M. U.; DUTTA, R.; HEEREN, D.M. Fuzzy control system for variable rate irrigation using remote sensing. Expert Systems with Applications, v. 124, p. 13-24, 2019. DOI: https://doi.org/10.1016/j.eswa.2019.01.043; Disponível em: < https://www.sciencedirect.com/science/article/pii/S0957417419300491>. Acesso em: 29 set. 2020.

MILES, C.; SMITH, N. What grows in Silicon Valley?: the emerging ideology of food technology. DAVIS, L. D.; PILGRIM, K.; SINHA, M. (Org.) The ecopolitics of consumption: the food trade. Lanham-USA: Rowman & Littlefield, 2016. Cap. 8, p. 119-138.

MINIZ, S.; SAHA, A.; DEV, M. R. Arduino based automatic irrigation system. ADBU Journal of Electrical and Electronics Engineering (AJEEE), v.3, n. 1, p.31-35, 2019.

MOHAMMADI, B.; RANJBAR, S. F.; AJABSHIRCHI, Y. Application of dynamic model to predict some inside environment variables in a semi-solar greenhouse. Information Processing in Agriculture, v. 5, n. 2, p. 279-288, 2018. DOI: https://doi.org/10.1016/j.inpa.2018.01.001; Disponível em: . Acesso em: 29 set. 2020.

MOUSA, A. K.; ABDULLAH, M. N. Fuzzy based Decision Support Model for Irrigation System Management. International Journal of Computer Applications. v. 104, n. 9, p. 14-20, 2014. Disponível em: . Acesso em: 12 set. 2020.

PATIL, S. L.; TANTAU, H. J.; SALOKHE, V. M. Modelling of tropical greenhouse temperature by auto regressive and neural network models. Biosystems Engineering, v. 99, n. 3, p. 423-431, 2008. DOI: http://doi.org/10.1016/j.biosystemseng.2007.11.009; Disponível em: . Acesso em: 29 set. 2020.

PAULA, R. C. M.; SILVA, A. G.; COSTA, E.; BINOTTI, F. F. S. Monitoramento de variáveis micrometeorológicas em diferentes ambientes protegidos no período de inverno. Revista de Agricultura Neotropical, Cassilândia, v. 4, supl., p. 103-109, 2017. DOI: http://doi.org/10.32404/rean.v4i5.2210; Disponível em: . Acesso em: 12 set. 2020.

PÉREZ-ALONSO, J.; PÉREZ-GARCÍA, M.; PASAMONTES-ROMERA, M.; CALLEJÓN-FERRE, A. J. Performance analysis and neural modelling of a greenhouse integrated photovoltaic system. Renewable and Sustainable Energy Reviews, v. 16, n. 7, p. 4675-4685, 2012. DOI: http://doi.org/10.1016/j.rser.2012.04.002; Disponível em: . Acesso em: 12 set. 2020.

PRASAD, G.; BABU, A. PANI: An expert system for irrigation management. Georgian Electronic Scientific Journal, v. 12, n. 1, p. 40-44, 2007.

PROCHNOW, D.; CARON, B. O.; HEINZMANN, B. M.; GARLET, Q. I.; FONTANA, D. C.; SCHMIDT, D. Effect of meteorological elements on the content and composition of Aloysia triphylla (L´Hérit) Britton essential oil. Blacpma, v. 18, n. 3, p. 325-335, 2019. Disponível em: . Acesso em: 29 set. 2020.

RODRÍGUEZ, F.; BERENGUE, M.; GUZMÁN, J. L.; RAMÍREZ-ARIAS, A. Modeling and control of greenhouse crop growth. Suíça: Springer, Cham, 2015.

ROSÁRIO, J. M. Automação Industrial. São Paulo: Baraúna, 2009.

SAAD, S. M.; KAMARUDIN, L. M.; KAMARUDIN, K.; NOORIMAN, W. M.; MAMDUH, S. M.; ZAKARIA, A.; SHAKAFF, A. Y. M.; JAAFAR, M. N. A real-time greenhouse monitoring system for mango with Wireless Sensor Network (WSN). 2nd International Conference on Electronic Design (ICED), p. 521-526, 2014.

SAAVOSS, M.; MAJSZTRIK, J.; BELAYNEH, B.; LEA-COX, J.; LICHTENBERG, E. Yield, quality and profitability of sensor-controlled irrigation: A case study of snapdragon (Antirrhinum majus L.) production. Irrigation Science, v. 34, p. 409-420, 2016. DOI: https://doi.org/10.1007/s00271-016-0511-y; Disponível em: . Acesso em: 29 set. 2020.

SERÔDIO, C.; BOAVENTURA CUNHA, J.; MORAIS, R.; COUTO, C.; MONTEIRO, J. A networked platform for agricultural management systems. Computers and Electronics in Agriculture, v. 31, v. 1, p. 75-90, 2001. DOI: http://doi.org/10.1016/S0168-1699(00)00175-7; Disponível em: . Acesso em: 29 set. 2020.

SHAMSHIRI, R. R.; KALANTARI, F.; TING, K. C.; THORP K. R.; HAMEED, I. A.; WELTZIEN, C.; AHMAD, D.; SHAD, Z. Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture. International Journal of Agricultural and Biological Engineering, v. 11, n. 1, p. 1-22, 2018. DOI: http://doi.org/10.25165/j.ijabe.20181101.3210; Disponível em: . Acesso em: 12 set. 2020.

SHAUGHNESSY, S. A.; EVETT, S. R.; COLAIZZI, P. D.; HOWELL, T. A. A crop water stress index and time threshold for automatic irrigation scheduling of grain sorghum. Agricultural Water Management, v. 107, p. 122-132, 2012. DOI: http://doi.org/10.1016/j.agwat.2012.01.018; Disponível em: . Acesso em: 12 set. 2020.

SILVA, A. G.; COSTA, E.; PEREIRA, T. C. C., BINOTTI, F. F. S., SCALOPI JUNIOR, E. J.; ZOZ, T. Quality of rubber tree rootstock seedlings grown in protected environments and alternative substrates. Acta Scientiarum. Agronomy, Maringá, v. 42, n. 1, e43469, 2020. DOI: http://doi.org/10.4025/actasciagron.v42i1.43469; Disponível em: . Acesso em: 29 set. 2020.

SILVA, B. L. B.; COSTA, E.; BINOTTI, F. F. S.; BENETT, C. G. S.; SILVA, A. G. Growth and quality of Garcinia humilis seedlings as a function of substrate and shading level. Pesquisa Agropecuária Tropical, Goiânia, v. 48, n. 4, p. 407-413, 2018. DOI: https:// doi: 10.1590/1983-40632018v4853500; Disponível em: . Acesso em: 29 set. 2020.

SONAIL, D. G.; DINESH, V. R. Soil Parameters Monitoring with Automatic Irrigation System. International Journal of Science, Engineering and Technology Research, vol. 4, n.11, p. 3817-3820, 2015. https:// Disponível em: . Acesso em: 29 set. 2020.

SONG, Y.; GONG, C.; FENG, Y.; MA, J.; ZHANG, X. Design of greenhouse control system based on wireless sensor networks and AVR microcontroller. Journal of Networks, v. 6, n. 12, p. 1668-1674, 2011. Disponível em: . Acesso em: 29 set. 2020.

SOUSA, A. V.; ROCHA R. V. O uso da automação para aprimorar o cultivo do pequeno produtor. RECoDAF - Revista Eletrônica Competências Digitais para Agricultura Familiar, v. 6, n. 1, p.20-41, 2020. Disponível em: . Acesso em: 29 set. 2020.

STORY, D.; KACIRA, M. Automated machine vision guided plant monitoring system for greenhouse crop diagnostics. Acta Horticulturae, v. 1037, p. 635-641, 2014. DOI: https://doi.org/10.17660/ActaHortic.2014.1037.81; Disponível em: . Acesso em: 29 set. 2020.

SUBRAMANIAN, V.; BURKS, T. F.; ARROYO, A. A. Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation. Computers and Electronics in Agriculture, v. 53, n. 2, p. 130-143, 2006. DOI: https://doi.org/10.1016/j.compag.2006.06.001; Disponível em: . Acesso em: 29 set. 2020.

TAIZ, L.; ZEIGER, E.; MOLLER, I. M.; MURPHY, A. 2017. Fisiologia e desenvolvimento vegetal. 6th. ed. Porto Alegre: Artmed, 2017. 888 p.

TAKI, M.; AJABSHIRCHI, Y.; RANJBAR, S. F.; ROHANI, A.; MATLOOBI, M. Heat transfer and MLP neural network models to predict inside environment variables and energy lost in a semi-solar greenhouse. Energy and Buildings, v. 110, n. 1, p. 314-329, 2016. DOI: https://doi.org/10.1016/j.enbuild.2015.11.010; Disponível em: . Acesso em: 29 set. 2020.

TAKI, M.; HADDAD, M. A Novel method with multilayer feed-forward neural network for modeling output yield in agriculture. International Journal of Modern Agriculture, v. 1, n. 1, p. 13-23, 2012. Disponível em: . Acesso em: 29 set. 2020.

TREJO-PEREA, M.; HERRERA-RUIZ, G.; RIOS-MORENO, J.; MIRANDA, R. C.; RIVAS-ARAIZA, E. Greenhouse energy consumption prediction using neural networks models. International Journal of Agriculture and Biology, v. 11, p. 1-6, 2009. Disponível em: . Acesso em: 29 set. 2020.

VAN HENTEN, E. J.; HEMMING, J.; VAN TUIJL, B. A. J.; KORNET, J. G.; MEULEMAN, J.; BONTSEMA, J.; VAN OS, E. A. An Autonomous robot for harvesting cucumbers in greenhouses. Autonomous Robots, v. 13, n. 1, p. 241-258, 2002. DOI: http://doi.org/10.1023/A%3A1020568125418; Disponível em: . Acesso em: 12 set. 2020.

VATARI, S.; BAKSHI, A.; THAKUR, T. Green house by using IOT and cloud computing. Internacional Conference on recent trends in eletronics information communication technology, 2016. DOI: http://doi.org/10.1109/RTEICT.2016.7807821; Disponível em: . Acesso em: 12 set. 2020.

VYTLA, S. J.; AHAMED, S. F. Smart wireless sensor network for automated greenhouse. IETE Journal of Research, v. 61, n. 2, p. 180-185, 2015. DOI: https://doi.org/10.1080/03772063.2014.999834; Disponível em: . Acesso em: 29 set. 2020.

WANG, H.; SÁNCHEZ-MOLINA, J.A.; LI, M.; BERENGUEL, M.; YANG, X. T.; BIENVENIDO, J. F. Leaf area index estimation for a greenhouse transpiration model using external climate conditions based on genetics algorithms, back-propagation neural networks and nonlinear autoregressive exogenous models. Agricultural Water Management, v. 183, p. 107-115, 2017. DOI: https://doi.org/10.1016/j.agwat.2016.11.021; Disponível em: . Acesso em: 29 set. 2020.

XANTHOPOULOS, G. T.; ATHANASIOU, A. A.; LENTZOU, D. I.; BOUDOUVIS, A. G.; LAMBRINOS, G. P. Modelling of transpiration rate of grape tomatoes. semi-empirical and analytical approach. Biosystems Engineering, v. 124, p. 16-23, 2014. DOI: https://doi.org/10.1016/j.biosystemseng.2014.06.005; Disponível em: . Acesso em: 29 set. 2020.

YAHYA, N. Agricultural 4.0: its implementation toward future sustainability. In: Green urea: Green Energy and Technology. New York, NY, USA, Springer, 2018. DOI: Singapore. https://doi.org/10.1007/978-981-10-7578-0_5. Disponível em: . Acesso em: 29 set. 2020.

YANG, H.; LIU, Q. F.; YANG, H. Q. Deterministic and stochastic modelling of greenhouse microclimate. Systems Science & Control Engineering, v. 7, n., 3, p. 65-72, 2019. DOI: https://doi.org/10.1080/21642583.2019.1661310; Disponível em: . Acesso em: 29 set. 2020.




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