https://doi.org/10.4081/jae.2020.1105
Wireless sensor network to identify the reduction of meteorological gradients in greenhouse in subtropical conditions
Published: 13 November 2020
Abstract Views: 1909
PDF: 558
HTML: 16
HTML: 16
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Spatial and temporal monitoring of temperature and relative humidity is essential for greenhouse management, therefore, wireless sensor networks (WSN) can offer crucial advantages. The objective of this work was to use a WSN to characterize and map the horizontal and vertical variability of air temperature and relative humidity inside a greenhouse using five different configurations. The configurations were based on combinations between the following actuating mechanisms: i) mechanical ventilation (by two exhaust fans); ii) natural ventilation (through the roof vent openings); iii) shading through the use of thermo-reflective screen. The WSN was designed with 45 spatially distributed measuring points, and the air temperature and relative humidity were recorded automatically every 30 seconds, for ten consecutive days, for each configuration. Our results show that the horizontal and vertical homogeneity of the meteorological elements depends on the actuating mechanism used in the greenhouse. Mechanical ventilation approximated the temperature and relative humidity of the indoor and outdoor air, with a homogeneous horizontal distribution throughout the environment. Opening the roof vent reduced vertical gradients of temperature and relative humidity. Our observations also showed that the combination of the use of roof vent openings with mechanical ventilation is an effective way to achieve horizontal homogeneity of meteorological elements.
Balendonck J., van Os E.A., van der Schoor R., van Tuijl B.A.J., Keizer L.C.P. 2010. Monitoring spatial and temporal distribution of temperature and relative humidity in greenhouses based on wireless sensor technology. Proc. International Conference on Agricultural Engineering, AgEng 2010: towards environmental technologies. Wageningen University and Research Center, Clermont-Ferrand, France, 443p.
Balendonck J., Sapounas A.A., Kempkes F., van Os E.A., van der Schoor R., van Tuijl B.A.J., Keizer L.C.P. 2014. Using a wireless sensor network to determine climate heterogeneity of a greenhouse environment. Acta Hortic. 1037:539-46.
DOI: https://doi.org/10.17660/ActaHortic.2014.1037.67
Bojacá C.R., Gil R., Gómez S., Cooman A., Schrevens E. 2009. Analysis of greenhouse air temperature distribution using geostatistical methods. Trans. ASABE. 52:957-68.
DOI: https://doi.org/10.13031/2013.27393
Chen C. 2003. Prediction of longitudinal variations in temperature and relative humidity for evaporative cooling greenhouses. Agricult. Engine. J. 12:143-64.
Coomans M., Allaerts K., Wittemans L., Pinxteren D. 2013. Monitoring and energetic performance of two similar semi-closed greenhouse ventilation systems. Energy Convers. Manage. 76:128-36.
DOI: https://doi.org/10.1016/j.enconman.2013.07.028
GarcÃa-Ruiz R.A., López-MartÃnez J., Blanco-Claraco J.L,. Pérez-Alonso J., Callejón-Ferre A.J. 2018. On air temperature distribution and ISO 7726-defined heterogeneity inside a typical greenhouse in AlmerÃa. Comput. Electron. Agricult. 151:264-75.
DOI: https://doi.org/10.1016/j.compag.2018.06.001
Ferentinos K.P., Katsoulas N., Tzounis A., Bartzanas T., Kittas C. 2017. Wireless sensor networks for greenhouse climate and plant condition assessment. Biosyst. Engine. 153:70-81.
DOI: https://doi.org/10.1016/j.biosystemseng.2016.11.005
Kittas C., Katsoulas N., Papa K., Thanasenaris A., Bartzanas T. 2012. Improvement of greenhouse microclimate distribution by means of air mixing fans. Acta Horticult. 927:589-94.
DOI: https://doi.org/10.17660/ActaHortic.2012.927.72
Kutta E., Hubbart J. 2014. Improving understanding of microclimate heterogeneity within a contemporary plant growth facility to advance climate control and plant productivity. J. Plant Sci. 2:167-78.
López A., Valera D.L., Molina-Aiz F.D. 2011. Sonic anemometry to measure natural ventilation in greenhouses. Sensors. 11:9820-38.
DOI: https://doi.org/10.3390/s111009820
López A., Valera D.L., Molina-Aiz F.D., Peña A. 2013. Effectiveness of horizontal air flow fans supporting natural ventilation in a Mediterranean multi-span greenhouse. Sci. Agric. 70:219-28.
DOI: https://doi.org/10.1590/S0103-90162013000400001
López-MartÃnez J., Blanco-Claraco J.L., Pérez-Alonso J., Callejón-Ferre A.J. 2018. Distributed network for measuring climatic parameters in heterogeneous environments: application in a greenhouse. Comput. Electron. Agricult. 145:105-21.
DOI: https://doi.org/10.1016/j.compag.2017.12.028
Narasimhan V.L., Arvind A.A., Bever K. 2007. Greenhouse asset management using wireless sensor-actor networks. Proc. International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies. DOI: 10.1109/UBICOMM.2007.43
DOI: https://doi.org/10.1109/UBICOMM.2007.43
Oz H., Atilgan A., Buyuktas K., Alagoz T. 2009. The efficiency of fan-pad cooling system in greenhouse and building up of internal greenhouse temperature map. Afr. J. Biotechnol. 8:5436-44.
Pawlowski A., Guzman J.L., RodrÃguez F., Berenguel M., Sánchez J., Dormido S. 2009. Simulation of greenhouse climate monitoring and control with wireless sensor network and event-based control. Sensors. 9:232-52.
DOI: https://doi.org/10.3390/s90100232
Sapounas A.A., Nikita-Martzopoulou C., Spiridis A. 2008. Prediction the spatial air temperature distribution of an experimental greenhouse using geostatistical methods. Acta Hortic. 801:495-500.
DOI: https://doi.org/10.17660/ActaHortic.2008.801.54
Stichting Milieukeur. 2010. Certification Groen Label Kas - Level B = CertificatieschemaGroen Label Kas - Niveau B - 2010 (in Dutch). Available from: http://www.smk.nl Accessed: Aug 15, 2014.
Suay R., López S., Granell R., Moltó E., Fatnassi H., Boulard T. 2008. Preliminary analysis of greenhouse microclimate heterogeneity for different weather. Acta Hortic. 797:103-9.
DOI: https://doi.org/10.17660/ActaHortic.2008.797.12
Tanny J. 2013. Microclimate and evapotranspiration of crops covered by agricultural screens: A review. Biosyst. Engine. 114:26-43.
DOI: https://doi.org/10.1016/j.biosystemseng.2012.10.008
Teitel M., Atias M., Barak M. 2010. Gradients of temperature, humidity and CO2 along a fan-ventilated greenhouse. Biosyst. Engine. 106:166-74.
DOI: https://doi.org/10.1016/j.biosystemseng.2010.03.007
Vox G., Losito P., Valente F., Consoletti R., Scarascia-Mugnozza G., Schettini E., Marzocca C., Corsi F. 2014. A wireless telecommunications network for real-time monitoring of greenhouse microclimate. J. Agric. Engine. XLV:237.
DOI: https://doi.org/10.4081/jae.2014.237
WMO (World Meteorological Organization). 1953. Commission for synoptic meteorology. Secretariat of the World Meteorological Organization, Geneva, Switzerland.
Zorzeto T.Q., Leal P.A.M. 2017. Wireless sensor network to map the meteorological variability in a greenhouse with evaporative cooling. Acta Hortic. 1154:213-20.
How to Cite
Queiroz Zorzeto Cesar, T. (2020) “Wireless sensor network to identify the reduction of meteorological gradients in greenhouse in subtropical conditions”, Journal of Agricultural Engineering, 52(1). doi: 10.4081/jae.2020.1105.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Giuliano Vox, Pierfrancesco Losito, Fabio Valente, Rinaldo Consoletti, Giacomo Scarascia-Mugnozza, Evelia Schettini, Cristoforo Marzocca, Francesco Corsi, A wireless telecommunications network for real-time monitoring of greenhouse microclimate , Journal of Agricultural Engineering: Vol. 45 No. 2 (2014)
- Raffaele Cavalli, Stefano Grigolato, Marco Pellegrini, The evolution of a mountain road network from its original use during the First World War to meeting today’s forestry needs: current management , Journal of Agricultural Engineering: Vol. 43 No. 3 (2012)
- Marzia Quattrone, Giovanna Tomaselli, Lara Riguccio, Patrizia Russo, Assessment of the territorial suitability for the creation of the greenways networks: Methodological application in the Sicilian landscape context , Journal of Agricultural Engineering: Vol. 48 No. 4 (2017)
- Marko Milan Kostić, Nataša Ljubičić, Vladimir Aćin, Milan Mirosavljević, Maša Budjen, Miloš Rajković, Nebojša Dedović, An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Pankaj Tyagi, Rahul Semwal, Anju Sharma, Uma Shanker Tiwary, Pritish Varadwaj, E-nose: a low-cost fruit ripeness monitoring system , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- Martin Thalheimer, A leaf-mounted capacitance sensor for continuous monitoring of foliar transpiration and solar irradiance as an indicator of plant water status , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- Xiong Bi, Hongchun Wang, Double-branch deep convolutional neural network-based rice leaf diseases recognition and classification , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Ziyan Fan, Lijun Li, Zicheng Gao, Fuzzy neural network PID control design of camellia fruit vibration picking manipulator , Journal of Agricultural Engineering: Vol. 54 No. 2 (2023)
- Paolo Liberati, Paolo Zappavigna, Evaluation of solar energy on the roofs of livestock houses , Journal of Agricultural Engineering: Vol. 43 No. 4 (2012)
- Yun Zhu, Shuwen Liu, Xiaojun Wu, Lianfeng Gao, Youyun Xu, Multi-class segmentation of navel orange surface defects based on improved DeepLabv3+ , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
You may also start an advanced similarity search for this article.
