Possibilities of IoT based management system in greenhouses
Keywords:
internet of things, sensor networks, environmental data acquisition, greenhouse, decision supportAbstract
The widespread use of information technologies provides new opportunities for agriculture to achieve optimal results. The IoT (Internet of Things) and Big Data, parts of the Industry 4.0 concept, provide methods to establish primary databases adjusted to the needs of the specific farm. Previously, several modular, multifunctional data acquisition systems have been developed, to measure factors at multiple spatial points. In the following, an experimental, cost-effective greenhouse management system will be presented, based on the 5th generation reference system. During the first test, the system provided multi-point environmental data to discover the characteristics of the greenhouse, to meet the decisionmakers requirements and to use it as basis for controlling, thereby ensuring optimal environment during the production. To store measurement and additional data, describing the farm for management purposes, databases and a module for the web-based application was developed to ensure data integrity. The application provides functions to manage the system and to analyze as well as visualize data through ETL processes. In the paper, the initial steps of the experiment have been presented, including the opportunities, the sensor network, the management application and a test to gather real-time experience to support the development of further features.
References
Aiello, G., Giovino, I., Vallone, M., Catania, P., Argento, A. 2018. A decision support system based on multisensor data fusion for sustainable greenhouse management. Journal of Cleaner Production. 172, 4057–4065. https://doi.org/10.1016/j.jclepro.2017.02.197
Akkaş, M. A., Sokullu, R. 2017. An IoT-based greenhouse monitoring system with Micaz motes. In International Workshop on IoT, M2M and Healthcare (IMH 2017). https://doi.org/10.1016/j.procs.2017.08.300
Atia, D. M., El-madany, H. T. 2016. Temperature control based on ANFIS. Journal of Electrical Systems and Information Technology. 4 (1) 34–48. https://doi.org/10.1016/j.jesit.2016.10.014
Boissard, P., Martin, V., Moisan, S. 2008. A cognitive vision approach to early pest detection in greenhouse crops. Computers and Electronics in Agriculture. 62 (2) 81–93. https://doi.org/10.1016/j.compag.2007.11.009
Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., Liakos, B., Canavari, M., Wiebensohn, J., Tisserye, B. 2015. Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture. 115, 40–50. https://doi.org/10.1016/j.compag.2015.05.011
Garcia, D. 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis. 54 (4) 1167–1178. https://doi.org/10.1016/j.csda.2009.09.020
Gebbers, R., Adamchuk, V. I. 2010. Precision agriculture and food security. Science. 327 (5967) 828–831. https://doi.org/10.1126/science.1183899
Geng, H. 2017. Internet of Things and Data Analytics Handbook. Wiley. ISBN: 978-1-119-17364-9 https://doi.org/10.1002/9781119173601.ch1
Huang, Y. 2013. Automatic process control for the food industry: an introduction, Woodhead Publishing Limited. 3-20 ISBN: 978-1-84569-801-0 https://doi.org/10.1533/9780857095763.1.3
Lin, J.-S., Liu, C.-Z. 2008. A monitoring system based on wireless sensor network and an SoC platform in precision agriculture. In 2008 11th IEEE International Conference on Communication Technology, 101–104. https://doi.org/10.1109/ICCT.2008.4716133
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M. 2014. Industry 4.0, Wirtschaftsinformatik, 56, 261–264 https://doi.org/10.1007/s11576-014-0424-4
Liao, M. S., Chen, S. F., Chou, C. Y., Chen, H. Y., Yeh, S. H., Chang, Y. C., Jiang, J. A. 2017. On precisely relating the growth of Phalaenopsis leaves to greenhouse environmental factors by using an IoT-based monitoring system. Computers and Electronics in Agriculture. 136, 125–139. https://doi.org/10.1016/j.compag.2017.03.003
Luque, A., Peralta, M. E., de las Heras, A., Córdoba, A. 2017. State of the Industry 4.0 in the Andalusian food sector, In Manufacturing Engineering Society International Conference, MESIC 1199–1205. https://doi.org/10.1016/j.promfg.2017.09.195
Tóth, M., Felföldi, J., Szilágyi, R. 2018. IoT eszközök alkalmazása a döntéshozatal támogatására, International Journal of Engineering and Management Sciences 3 (4) 125–141. https://doi.org/10.21791/IJEMS.2018.4.12.
Naveen Balaji, G., Nandhini, V., Mithra, S., Priya, N., Naveena, R. 2018. IOT Based Smart Crop Monitoring in Farm Land. Imperial Journal of Interdisciplinary Research. 4 (1) 88–92.
Paraforos, D. S., Vassiliadis, V., Kortenbruck, D., Stamkopoulos, K., Ziogas, V., Sapounas, A. A., Griepentrog, H. W. 2017. Multi-level automation of farm management information systems. Computers and Electronics in Agriculture. 142, 504–514. https://doi.org/10.1016/j.compag.2017.11.022
Park, D. H., Park, J. W. 2011. Wireless sensor network-based greenhouse environment monitoring and automatic control system for dew condensation prevention. Sensors. 11 (4) 3640–3651. https://doi.org/10.3390/s110403640
Patrício, D. I., Rieder, R. 2018. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture. 153, 69–81. https://doi.org/10.1016/j.compag.2018.08.001
Rajendra, P., Kondo, N., Ninomiya, K., Kamata, J., Kurita, M., Shiigi, T., Hayashi, S., Yoshida H., Kohno, Y. 2009. Machine vision algorithm for robots to harvest strawberries in tabletop culture greenhouses. Engineering in Agriculture, Environment and Food. 2 (1) 24–30. https://doi.org/10.1016/S1881-8366(09)80023-2
Rupnik, R., Kukar, M., Vračar, P., Košir, D., Pevec, D., Bosnić, Z. 2018. AgroDSS: A decision support system for agriculture and farming. Computers and Electronics in Agriculture. 161, 260–271. https://doi.org/10.1016/j.compag.2018.04.001
Tilman, D., Balzer, C., Hill, J., Befort, B. L. 2011. Global food demand and the sustainable intensification of agriculture. In Proceedings of the National Academy of Sciences. 108 (50) 20260–20264. https://doi.org/10.1073/pnas.1116437108
Yick, J., Mukherjee, B., Ghosal, D. 2008. Wireless sensor network survey, Computer Networks, 52 (12) 2292–2330. https://doi.org/10.1016/j.comnet.2008.04.002
Zhou, H. 2013. The Internet of Things in the Cloud. A middleware Perspective, CRC Press ISBN 9781439892992
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