Abstract
Purpose
Sensors are the primary component of a monitoring and control system. Effective monitoring and control of the microclimatic environment in a greenhouse is the key necessity for protecting crops from adverse environments. Moreover,the greenhouse microclimate is influenced by various factors. In the large-scale greenhouse facilities, several sensors and actuators are needed to control the system. Manual monitoring and control of such a large and complex system is labor-intensive and impractical. Therefore, an automatic monitoring and control system in the greenhouse becomes indispensable. In addition, microclimatic parameters such as temperature, humidity, and solar irradiance in the greenhouse are non-linearly interlinked, thereby forming a non-linear multivariate system. Thus, an appropriately designed sensor system is needed for monitoring and controlling the greenhouse microclimate.
Methods
Research articles on greenhouse microclimate monitoring and control published in the last 6 years were considered. The sensor devices and technologies applied to control particular environmental parameters in the greenhouse and their key achievements were systematically reviewed. In addition, different approaches to determine the optimum number of sensors and their placement inside the greenhouse were investigated.
Results
It was found that spatially installed sensor devices above the plant height reflect the actual information of the environment getting by plant. Furthermore, both hardware and software-based sensing techniques control the greenhouse microclimate optimally. The proper positioning of sensors and their protection from harsh environmental factors is also essential.
Conclusions
It can be concluded that modern sensor devices and systems are driving the greenhouse monitoring and control system toward an intelligent, real-time, remotely accessible, and fully automatic system.
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References
Abas, M. A., & Dahlui, M. (2016). Development of greenhouse autonomous control system for Home Agriculture project. In ICAMIMIA 2015 - International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation, Proceeding - In conjunction with Industrial Mechatronics and Automation Exhibition, IMAE, 2015(Icamimia) (pp. 12–17, Surabaya, Indonesia). https://doi.org/10.1109/ICAMIMIA.2015.7507993.
Achouak, T., Khelifa, B., García, L., Parra, L., Lloret, J., & Fateh, B. (2019). Sensor network proposal for greenhouse automation placed at the south of Algeria. Network Protocols and Algorithms, 10(4), 53. https://doi.org/10.5296/npa.v10i4.14155.
Ahamed, M. S., Guo, H., & Tanino, K. (2019). Energy saving techniques for reducing the heating cost of conventional greenhouses. Biosystems Engineering, 178, 9–33. https://doi.org/10.1016/j.biosystemseng.2018.10.017.
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. Procedia Computer Science, 113, 603–608. https://doi.org/10.1016/j.procs.2017.08.300.
Al-Aubidy, K. M., Ali, M. M., Derbas, A. M., & Al-Mutairi, A. W. (2014). Real-time monitoring and intelligent control for greenhouses based on wireless sensor network. In 2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices (SSD 2014) (pp. 1–7). Barcelona, Spain: IEEE. https://doi.org/10.1109/SSD.2014.6808765.
Anandamurugan, S., & Rajasekaran, T. (2019). Challenges and applications of wireless sensor networks in smart farming—a survey. In Proceedings of ICBDCC18 (pp. 353–361). Singapore: Springer. https://doi.org/10.1007/978-981-13-1882-5.
Aqeel-Ur-Rehman, Abbasi, A. Z., Islam, N., & Shaikh, Z. A. (2014). A review of wireless sensors and networks’ applications in agriculture. Computer Standards and Interfaces, 36(2), 263–270. https://doi.org/10.1016/j.csi.2011.03.004.
Bajer, L., & Krejcar, O. (2015). Design and realization of low cost control for greenhouse environment with remote control. IFAC-PapersOnLine, 48(4), 368–373. https://doi.org/10.1016/j.ifacol.2015.07.062.
Bao, J., Lu, W.-H., Zhao, J., & Bi, X. T. (2018). Greenhouses for CO2 sequestration from atmosphere. Carbon Resources Conversion, 1(2), 183–190. https://doi.org/10.1016/j.crcon.2018.08.002.
Basak, J. K., Qasim, W., Okyere, F. G., Khan, F., Lee, J., Park, J., & Kim, H. T. (2019). Regression analysis to estimate morphology parameters of pepper plant in a controlled greenhouse system. Journal of Biosystems Engineering, 44(2), 57–68. https://doi.org/10.1007/s42853-019-00014-0.
Bogena, H. R., Huisman, J. A., Oberdörster, C., & Vereecken, H. (2007). Evaluation of a low-cost soil water content sensor for wireless network applications. Journal of Hydrology, 344(1–2), 32–42. https://doi.org/10.1016/j.jhydrol.2007.06.032.
Bonachela, S., González, A. M., & Fernández, M. D. (2006). Irrigation scheduling of plastic greenhouse vegetable crops based on historical weather data. Irrigation Science, 25(1), 53–62. https://doi.org/10.1007/s00271-006-0034-z.
Borrero, J. D., & Zabalo, A. (2020). An autonomous wireless device for real-time monitoring of water needs. Sensors (Switzerland), 20(7), 1–16. https://doi.org/10.3390/s20072078.
Chacko, S., & Job, M. D. (2018). Security mechanisms and vulnerabilities in LPWAN. In IOP Conference Series: Materials Science and Engineering, 396(1). Kerala State, India: IOP Science. https://doi.org/10.1088/1757-899X/396/1/012027.
Chandrasekaran, N., Somanah, R., Rughoo, D., Dreepaul, R. K., Tyagaraja, S., Cunden, M., & Demkah, M. (2019). Digital transformation from leveraging blockchain technology, artificial intelligence, machine learning and deep learning. Adv. Intell. Syst. Comput., 863, 271–283. https://doi.org/10.1007/978-981-13-3338-5.
Chang, Y. S., Chen, Y. H., & Zhou, S. K. (2019). A smart lighting system for greenhouses based on Narrowband-IoT communication. In Proceedings of Technical Papers - International Microsystems, Packaging, Assembly, and Circuits Technology Conference, IMPACT, 2018-October (pp. 275–278). Taipei, Taiwan: IEEE. https://doi.org/10.1109/IMPACT.2018.8625804.
Chen, F., Qin, L., Li, X., Wu, G., & Shi, C. (2017). Design and implementation of ZigBee wireless sensor and control network system in greenhouse. 2017 36th Chinese Control Conference, CCC (pp. 8982–8986). IEEE: Dalian, China. https://doi.org/10.23919/ChiCC.2017.8028786.
Chen, Y., & Chien, H. (2017). IoT-based green house system with splunk data analysis. Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017, 2018-Janua (iCAST) (pp. 260–263). IEEE: Taichung, Taiwan. https://doi.org/10.1109/ICAwST.2017.8256458.
Chiu, Y. C., Yang, P. Y., & Grift, T. E. (2014). A wireless communication system for automated greenhouse operations. Engineering in Agriculture, Environment and Food, 7(2), 78–85. https://doi.org/10.1016/j.eaef.2014.02.003.
Codeluppi, G., Cilfone, A., Davoli, L., & Ferrari, G. (2020). LoraFarM: a LoRaWAN-based smart farming modular IoT architecture. Sensors, 20(7), 2028. https://doi.org/10.3390/s20072028.
Datta, S., Taghvaeian, S., Ochsner, T. E., Moriasi, D., Gowda, P., & Steiner, J. L. (2018). Performance assessment of five different soil moisture sensors under irrigated field conditions in Oklahoma. Sensors (Switzerland), 18(11), 1–17. https://doi.org/10.3390/s18113786.
Elanchezhian, A., Basak, J. K., Park, J., Khan, F., Okyere, F. G., Lee, Y., et al. (2020). Evaluating different models used for predicting the indoor microclimatic parameters of a greenhouse. Applied Ecology and Environmental Research, 18(2), 2141–2161. https://doi.org/10.15666/aeer/1802_21412161.
Erazo, M., Rivas, D., Perez, M., Galarza, O., Bautista, V., Huerta, M., & Rojo, J. L. (2015). Design and implementation of a wireless sensor network for rose greenhouses monitoring. In: ICARA 2015 - Proceedings of the 2015 6th International Conference on Automation, Robotics and Applications, pp. 256–261. Queenstown, New Zealand: IEEE. https://doi.org/10.1109/ICARA.2015.7081156.
FAO. (2018). The future of food and agriculture – alternative pathways to 2050. http://www.fao.org/3/I8429EN/i8429en.pdf. Accessed 12 Jan 2020.
Ferentinos, K. P., Katsoulas, N., Tzounis, A., Bartzanas, T., & Kittas, C. (2017). Wireless sensor networks for greenhouse climate and plant condition assessment. Biosystems Engineering, 153, 70–81. https://doi.org/10.1016/j.biosystemseng.2016.11.005.
Garg, A., Munoth, P., & Goyal, R. (2016). Application of soil moisture sensors in agriculture: a review. In Proceedings of International Conference on Hydraulics, Water Resources and Coastal Engineering (Hydro2016), CWPRS (pp. 1662–1672). Pune, India: HYDRO-2016.
Gonzales Perez, I., & Calderon Godoy, A. J. (2018). Neural networks-based models for greenhouse climate control. XXXIX Jornadas de Automática, (September) (pp. 875–879). Spain: Badajoz. https://doi.org/10.17979/spudc.9788497497565.0875.
Guzmán, C. H., Carrera, J. L., Durán, H. A., Berumen, J., Ortiz, A. A., Guirette, O. A., et al. (2019). Implementation of virtual sensors for monitoring temperature in greenhouses using CFD and control. Sensors, 19(1), 60. https://doi.org/10.3390/s19010060.
Hang, Z., Linda, S., Wangliang, L., Chuang, L., & Kaiyan, W. (2017). Application of multi-sensor data fusion technique in greenhouse environmental monitoring. Proceedings - 2017 International Conference on Smart Grid and Electrical Automation, ICSGEA 2017, 2017-Janua (pp. 51–55). Changsha, China: IEEE. https://doi.org/10.1109/ICSGEA.2017.47.
Henderson, S., Gholami, D., & Zheng, Y. (2018). Soil moisture sensor-based systems are suitable for monitoring and controlling irrigation of greenhouse crops. HortScience, 53(4), 552–559. https://doi.org/10.21273/HORTSCI12676-17.
Hongkang, W., Li, L., Yong, W., Fanjia, M., Haihua, W., & Sigrimis, N. A. (2018). Recurrent neural network model for prediction of microclimate in solar greenhouse. IFAC-PapersOnLine, 51(17), 790–795. https://doi.org/10.1016/j.ifacol.2018.08.099.
Huynh, T. (2015). Thermal sensors. In Smart Sensor Systems (pp. 5–42). Berlin: Springer. https://doi.org/10.1002/9780470866931.ch6.
Ismail, M. T., Ismail, M. N., Sameon, S. S., Zin, Z. M., & Mohd, N. (2017). Wireless sensor network: smart greenhouse prototype with smart design. 2nd International Symposium on Agent, Multi-Agent Systems and Robotics, ISAMSR 2016, (August) (pp. 57–62). Bangi, Malaysia: IEEE. https://doi.org/10.1109/ISAMSR.2016.7810003.
Jaihuni, M., Basak, J. K., Khan, F., Okyere, F. G., Arulmozhi, E., Bhujel, A., et al. (2020). A partially amended hybrid Bi-Gru—ARIMA model (PAHM) for predicting solar irradiance in short and very-short terms. Energies, 13(2), 435. https://doi.org/10.3390/en13020435.
Jermin Jeaunita, T. C., & Sarasvathi, V. (2019). Fault tolerant sensor node placement for IoT based large scale automated greenhouse system. International Journal of Computing and Digital Systems, 8(2), 189–197. https://doi.org/10.12785/ijcds/080210.
Kaburuan, E. R., Jayadi, R., & Harisno. (2019). A design of IoT-based monitoring system for intelligence indoor micro-climate horticulture farming in Indonesia. Procedia Computer Science, 157, 459–464. https://doi.org/10.1016/j.procs.2019.09.001.
Kaiser, E., Ouzounis, T., Giday, H., Schipper, R., Heuvelink, E., & Marcelis, L. F. M. (2019). Adding blue to red supplemental light increases biomass and yield of greenhouse-grown tomatoes, but only to an optimum. Frontiers in Plant Science, 9(January), 1–11. https://doi.org/10.3389/fpls.2018.02002.
Kaneda, Y., Ibayashi, H., Oishi, N., & Mineno, H. (2015). Greenhouse environmental control system based on SW-SVR. Procedia Computer Science, 60(1), 860–869. https://doi.org/10.1016/j.procs.2015.08.249.
Khanna, A., & Kaur, S. (2019). Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture. Computers and Electronics in Agriculture, 157(November 2018), 218–231. https://doi.org/10.1016/j.compag.2018.12.039.
Kim, J. Y., & Sangcheol Kim, J. L. (2018). Comparative analysis of TTAK.KO-06.0288-Part3 and development of an open-source communication library for greenhouse control system. Journal of Biosystems Engineering, 43(1), 72–80. https://doi.org/10.5307/JBE.2018.43.1.072.
Kochhar, A., & Kumar, N. (2019). Wireless sensor networks for greenhouses: an end-to-end review. Computers and Electronics in Agriculture, 163(June), 104877. https://doi.org/10.1016/j.compag.2019.104877.
Kuglestadt, T. (2015). RTDs and thermistors in building automation, (April), 1–11. Texas Instrumentation. https://www.semiee.com/file/TI/TI-LMT90-NOTES.pdf. Accessed on 25 March 2020.
Labs, S. (2013). The evolution of wireless sensor networks, 1.0, 1–5. Silicon Labs. https://www.silabs.com/documents/public/white-papers/evolution-of-wireless-sensor-networks.pdf. Accessed on 07 June 2020.
Lata, S. H. K. V. (2017). Selection of sensor number and locations in intelligent greenhouse. In 2017 3rd International Conference on Condition Assessment Techniques in Electrical Systems (CATCON) (p. 5). India: Rupnagar. https://doi.org/10.1109/CATCON.2017.8280184.
Lauridsen, M., Nguyen, H., Vejlgaard, B., Kovacs, I. Z., Mogensen, P., & Sorensen, M. (2017). Coverage comparison of GPRS, NB-IoT, LoRa, and SigFox in a 7800 km area. In IEEE Vehicular Technology Conference, 2017-June (pp. 2–6). https://doi.org/10.1109/VTCSpring.2017.8108182.
Lee, C. K., Chung, M., Shin, K.-Y., Im, Y.-H., & Yoon, S.-W. (2019a). A study of the effects of enhanced uniformity control of greenhouse environment variables on crop growth. Energies, 12, 1749. https://doi.org/10.3390/en12091749.
Lee, S. Y., Lee, I. B., Yeo, U. H., Kim, R. W., & Kim, J. G. (2019b). Optimal sensor placement for monitoring and controlling greenhouse internal environments. Biosystems Engineering, 188, 190–206. https://doi.org/10.1016/j.biosystemseng.2019.10.005.
Li, T., & Yang, Q. (2015). Advantages of diffuse light for horticultural production and perspectives for further research. Frontiers in Plant Science, 6(september), 1–5. https://doi.org/10.3389/fpls.2015.00704.
Liang-Ying, Guo, Y. F., & Zhao-Wei. (2015). Greenhouse environment monitoring system design based on WSN and GPRS networks. In 2015 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015 (pp. 795–798). China: Shenyang. https://doi.org/10.1109/CYBER.2015.7288044.
Liang, M. H., He, Y. F., Chen, L. J., & Du, S. F. (2018). Greenhouse environment dynamic monitoring system based on WIFI. IFAC-PapersOnLine, 51(17), 736–740. https://doi.org/10.1016/j.ifacol.2018.08.108.
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.
Lin, B., Recke, B., Knudsen, J. K. H., & Jørgensen, S. B. (2007). A systematic approach for soft sensor development. Computers and Chemical Engineering, 31(5–6), 419–425. https://doi.org/10.1016/j.compchemeng.2006.05.030.
Liu, L., & Jiang, W. (2018). Design of vegetable greenhouse monitoring system based on ZigBee and GPRS. Proceedings - 2018 4th International Conference on Control, Automation and Robotics, ICCAR 2018 (pp. 336–339). Auckland, New Zealand: IEEE. https://doi.org/10.1109/ICCAR.2018.8384696.
Liu, L., & Zhang, Y. (2017). Design of greenhouse environment monitoring system based on Wireless Sensor Network. 2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017 (pp. 463–466). Nagoya, Japan: IEEE. https://doi.org/10.1109/ICCAR.2017.7942739.
Liu, D., Cao, X., Huang, C., & Ji, L. (2016). Intelligent agriculture greenhouse environment monitoring system based on IOT technology. In Proceedings - 2015 International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2015, 487–490. Halong Bay, Vietnam: IEEE. https://doi.org/10.1109/ICITBS.2015.126.
Mainetti, L., Patrono, L., & Vilei, A. (2011). Evolution of wireless sensor networks towards the Internet of Things: a survey. In SoftCOM 2011, 19th International Conference on Software, Telecommunications and Computer Networks (pp. 1–6). Split, Croatia: http://www.scopus.com/inward/record.url?eid=2-s2.0-81455142290&partnerID=40&md5=8089ed723b1c1056c9a6ae8fa767fa4f. Accessed 22 Jan 2020.
Martinović, G., & Simon, J. (2014). Greenhouse microclimatic environment controlled by a mobile measuring station. NJAS - Wageningen Journal of Life Sciences, 70, 61–70. https://doi.org/10.1016/j.njas.2014.05.007.
McGrath, M. J., Scanaill, C. N., McGrath, M. J., & Scanaill, C. N. (2013). Sensor network topologies and design considerations. Sensor Technologies, (pp. 79–95). Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4302-6014-1_4.
Mekki, K., Bajic, E., Chaxel, F., & Meyer, F. (2019). A comparative study of LPWAN technologies for large-scale IoT deployment. ICT Express, 5(1), 1–7. https://doi.org/10.1016/j.icte.2017.12.005.
Mohammadi, B., Ranjbar, S. F., & Ajabshirchi, Y. (2018). Application of dynamic model to predict some inside environment variables in a semi-solar greenhouse. Information Processing in Agriculture, 5(2), 279–288. https://doi.org/10.1016/j.inpa.2018.01.001.
Mukazhanov, Y., Kamshat, Z., Assel, O., Shayhmetov, N., & Alimbaev, C. (2017). Microclimate control in greenhouses. In: International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 17(62), pp. 699–704,. https://doi.org/10.5593/sgem2017/62/S27.089.
Muñoz, M., Guzmán, J. L., Sánchez, J. A., Rodríguez, F., & Torres, M. (2019). Greenhouse models as a service (GMaaS) for simulation and control. IFAC-PapersOnLine, 52(30), 190–195. https://doi.org/10.1016/j.ifacol.2019.12.520.
Neethirajan, S., Jayas, D. S., & Sadistap, S. (2009). Carbon dioxide (CO2) sensors for the agri-food industry-a review. Food and Bioprocess Technology, 2(2), 115–121. https://doi.org/10.1007/s11947-008-0154-y.
Nelson, P. V. (2003). Greenhouse operation and management. Upper Saddle River, NJ: Prentice Hall.
Noh, D. H., An, S. Y., & Kim, J. (2017). Implementation of optimal greenhouse control: multiple influences approach. International Conference on Ubiquitous and Future Networks, ICUFN (pp. 261–265). Milan, Italy: IEEE. https://doi.org/10.1109/ICUFN.2017.7993788.
Pahuja, R., Verma, H. K., & Uddin, M. (2017). An intelligent wireless sensor and actuator network system for greenhouse microenvironment control and assessment. Journal of Biosystems Engineering, 42(1), 23–43. https://doi.org/10.5307/jbe.2017.42.1.023.
Panwar, N. L., Kaushik, S. C., & Kothari, S. (2011). Solar greenhouse an option for renewable and sustainable farming. Renewable and Sustainable Energy Reviews, 15(8), 3934–3945. https://doi.org/10.1016/j.rser.2011.07.030.
Pawlowski, A., Sanchez, J. A., Guzman, J. L., Rodriguez, F., Berenguel, M., & Dormido, S. (2016). Event-based control for a greenhouse irrigation system. In 2016 2nd International Conference on Event-Based Control, Communication, and Signal Processing, EBCCSP 2016 - Proceedings. Krakow, Poland. https://doi.org/10.1109/EBCCSP.2016.7605236.
Prasad, B. V. G., & Chakravorty, S. (2015). Effects of climate change on vegetable cultivation-a review. Nature Environment and Pollution Technology, 14(4), 923–929.
Radha-Manohar, K., & Igathinathane, C. (2007). Greenhouse technology and management (2nd ed.). Hyderabad: BS Publications. https://doi.org/10.1079/9781780641034.0000.
Ramli, M. R., Daely, P. T., Kim, D. S., & Lee, J. M. (2020). IoT-based adaptive network mechanism for reliable smart farm system. Computers and Electronics in Agriculture, 170, 105287. https://doi.org/10.1016/j.compag.2020.105287.
Ramos-Fernández, J. C., Balmat, J. F., Márquez-Vera, M. A., Lafont, F., Pessel, N., & Espinoza-Quesada, E. S. (2016). Fuzzy modeling vapor pressure deficit to monitoring microclimate in greenhouses. IFAC-PapersOnLine, 49(16), 371–374. https://doi.org/10.1016/j.ifacol.2016.10.068.
Rehman, A. U., Abbasi, A. Z., & Shaikh, Z. A. (2008). Building a smart university using RFID technology. Proceedings - International Conference on Computer Science and Software Engineering, CSSE 2008, 5 (pp. 641–644). Hubei, China: IEEE. https://doi.org/10.1109/CSSE.2008.1528.
Rodríguez, S., Gualotuña, T., & Grilo, C. (2017). A system for the monitoring and predicting of data in precision agriculture in a rose greenhouse based on wireless sensor networks. Procedia Computer Science, 121, 306–313. https://doi.org/10.1016/j.procs.2017.11.042.
S.Asolkar, P., & S. Bhadade, U. (2014). Analyzing and predicting the green house parameters of crops. International Journal of Computer Applications, 95(15), 28–39. https://doi.org/10.5120/16672-6673.
Saha, T., Jewel, K. H., Mostakim, M. N., Bhuiyan, M. H., Ali, N. S., Rahman, M. K., et al. (2017). Construction and development of an automated greenhouse system using Arduino Uno. International Journal of Information Engineering and Electronic Business, 9(3), 1–8. https://doi.org/10.5815/ijieeb.2017.03.01.
Savvas, D., Gianquinto, G. P., Tüzel, Y., & Gruda, N. (2013). Good agricultural practices for greenhouse vegetable crops. FAO Plant Production and Protection Paper, 217. http://www.fao.org/3/a-i3284e.pdf. Accessed 16 Feb 2020.
Shamshiri, R. R., Jones, J. W., Thorp, K. R., Ahmad, D., Man, H. C., & Taheri, S. (2018). Review of optimum temperature, humidity, and vapour pressure deficit for microclimate evaluation and control in greenhouse cultivation of tomato: A review. International Agrophysics, 32(2), 287–302. https://doi.org/10.1515/intag-2017-0005.
Siddiqui, M. F., Ur Rehman Khan, A., Kanwal, N., Mehdi, H., Noor, A., & Khan, M. A. (2018). Automation and monitoring of greenhouse. 2017 International Conference on Information and Communication Technologies, ICICT 2017, 2017-Decem (pp. 197–201). IEEE: Karachi, Pakistan. https://doi.org/10.1109/ICICT.2017.8320190.
Singh, R. K., Berkvens, R., & Weyn, M. (2020). Energy Efficient Wireless Communication for IoT Enabled Greenhouses. In 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), 2020 (pp. 885–887). Bengaluru, India: IEEE. https://doi.org/10.1109/COMSNETS48256.2020.9027392.
Sinha, R. S., Wei, Y., & Hwang, S. H. (2017). A survey on LPWA technology: LoRa and NB-IoT. ICT Express, 3(1), 14–21. https://doi.org/10.1016/j.icte.2017.03.004.
Sri Jahnavi, V., & Ahamed, S. F. (2015). Smart wireless sensor network for automated greenhouse. IETE Journal of Research, 61(2), 180–185. https://doi.org/10.1080/03772063.2014.999834.
Sushanth, G., & Sujatha, S. (2018). IOT based smart agriculture system. 2018 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2018 (pp. 1–4). IEEE: Chennai, India. https://doi.org/10.1109/WiSPNET.2018.8538702.
Takeya, S., Muromachi, S., Maekawa, T., Yamamoto, Y., Mimachi, H., Kinoshita, T., Murayama, T., Umeda, H., Ahn, D. H., Iwasaki, Y., Hashimoto, H., Yamaguchi, T., Okaya, K., & Matsuo, S. (2017). Design of ecological CO2 enrichment system for greenhouse production using TBAB + CO2 semi-clathrate hydrate. Energies, 10(7), 1–11. https://doi.org/10.3390/en10070927.
Taki, M., Abdanan Mehdizadeh, S., Rohani, A., Rahnama, M., & Rahmati-Joneidabad, M. (2018). Applied machine learning in greenhouse simulation; new application and analysis. Information Processing in Agriculture, 5(2), 253–268. https://doi.org/10.1016/j.inpa.2018.01.003.
Taki, M., Ajabshirchi, Y., Ranjbar, S. F., Rohani, A., & Matloobi, M. (2016). Modeling and experimental validation of heat transfer and energy consumption in an innovative greenhouse structure. Information Processing in Agriculture, 3(3), 157–174. https://doi.org/10.1016/j.inpa.2016.06.002.
Türk, A. M., Gurel, U., Turk, A. M., & Sora Gunal, E. (2016). An automation system design for greenhouses by using DIY platforms. In The International Conference On Science, Ecology And Technology (Iconsete’2015 – Vienna) (pp. 257–266). Vienna, Austria.
United Nations, Department of Economic and Social Affairs, Population Division. (2019). World Population Prospects 2019: Highlights (ST/ESA/SER.A/423). https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdf. Accessed 10 Jan 2020.
Vimla, D. R., Khedo, K. K., & Bhoyroo, V. (2019). A flexible and reliable wireless sensor network architecture for precision agriculture in a tomato greenhouse. Adv. Intell. Syst. Comput., 863, 271–283. https://doi.org/10.1007/978-981-13-3338-5.
Vu, V. A., Cong Trinh, D., Truvant, T. C., & Dang Bui, T. (2018). Design of automatic irrigation system for greenhouse based on LoRa technology. International Conference on Advanced Technologies for Communications, 2018-October (pp. 72–77). IEEE: Ho Chi Minh City, Vietnam. https://doi.org/10.1109/ATC.2018.8587487.
Wang, J., Zhou, J., Gu, R., Chen, M., & Li, P. (2018). Manage system for internet of things of greenhouse based on GWT. Information Processing in Agriculture, 5(2), 269–278. https://doi.org/10.1016/j.inpa.2018.01.002.
Wilson, J. S. (2005). Sensor Technology Handbook. Burlington, MA 01803, USA: Elsevier Inc..
Wu, Y., Li, L., Li, M., Zhang, M., Sun, H., Sygrimis, N., & Lai, W. (2019). Remote-control system for greenhouse based on open source hardware. IFAC-PapersOnLine, 52(30), 178–183. https://doi.org/10.1016/j.ifacol.2019.12.518.
Xu, J., Dai, F., Xu, Y., Yao, C., & Li, C. (2019). Wireless power supply technology for uniform magnetic field of intelligent greenhouse sensors. Computers and Electronics in Agriculture, 156(April 2018), 203–208. https://doi.org/10.1016/j.compag.2018.11.014.
Yang, I. C., Hsieh, K. W., Tsai, C. Y., Huang, Y. I., Chen, Y. L., & Chen, S. (2014). Development of an automation system for greenhouse seedling production management using radio-frequency-identification and local remote sensing techniques. Engineering in Agriculture, Environment and Food, 7(1), 52–58. https://doi.org/10.1016/j.eaef.2013.12.009.
Zhang, L., Li, C., Jia, Y., & Xiao, Z. (2015). Design of greenhouse environment remote monitoring system based on Android platform. Chemical Engineering Transactions, 46, 739–744. https://doi.org/10.3303/CET1546124.
Zhang, Y., Chen, D., Wang, S., & Tian, L. (2018). A promising trend for field information collection: an air-ground multi-sensor monitoring system. Information Processing in Agriculture, 5(2), 224–233. https://doi.org/10.1016/j.inpa.2018.02.002.
Zou, Z., Bie, Y., & Zhou, M. (2018). Design of an intelligent control system for greenhouse. In Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018, (Imcec) (pp. 1632–1635). Xi’an, China: IEEE. https://doi.org/10.1109/IMCEC.2018.8469309.
Adafruit AM2302. AM2302 (Wired DHT22) temperature-humidity sensor. Available https://www.adafruit.com/product/393. Accessed 2 Feb 2020.
Adafruit DHT11. DHT11 basic temperature-humidity sensor. Available https://www.adafruit.com/product/386. Accessed 2 Feb 2020.
Adafruit DHT22. DHT22 temperature-humidity sensor + extras. Available https://www.adafruit.com/product/385. Accessed 2 Feb 2020.
Adept AVT. AVT Marlin F145-C2 Camera. Available https://www.adept.net.au/cameras/avt/pdf/MARLIN_F_145B_C.pdf. Accessed 9 Feb 2020.
Adept F145B. AVT Marlin F145-B2 Camera. Available https://www.adept.net.au/cameras/avt/pdf/MARLIN_F_145B_C.pdf. Accessed 9 Feb 2020.
Aosong AM2301. AM2301A temperature and humidity sensor module. Available https://www.aosong.com/products-28.html. Accessed 3 Feb 2020.
Apogeeinstruments SQ-110. SQ-110 Solar irradiance sensor. Available https://www.apogeeinstruments.com/sq-110-ss-sun-calibration-quantum-sensor/. Accessed 9 Feb 2020.
Apogeeinstruments SU-100. SU-100 Solar irradiance sensor. Available https://www.apogeeinstruments.com/su-100-ss-uv-sensor/#product-tab-description. Accessed 7 Feb 2020.
Artofcircuits FC-28. FC-28 Soil moisture sensor. Available https://www.artofcircuits.com/product/fc-28-soil-moisture-sensor-analog-and-digital-outputs. Accessed 7 Feb 2020.
Bdtic 29010. ISL 29010 Light sensor. Available https://www.bdtic.com/en/intersil/ISL29010. Accessed 5 Feb 2020.
Component LDR. Light Dependent Resistor. Available https://www.components101.com/ldr-datasheet. Accessed 5 Feb 2020.
Datasheet4u TGS4161. TGS 4161 CO2 sensor. Available https://www. datasheet4u.com/datasheet-pdf-file/842954/Figaro/TGS4161/1. Accessed 6 Feb 2020.
Datasheets DS18B20. DS18B20 Soil temperature and moisture sensor. Available https://www.datasheets.maximintegrated.com/en/ds/DS18B20-PAR.pdf. Accessed 8 Feb 2020.
Deltaohm 9009t. HD900TR humidity sensor. Available https://www.deltaohm.com/en/product/hd9008t-9009t-serie-meteorological-temperature-and-rh-transmitter. Accessed 3 Feb 2020.
Dwyer 657. DWYER 657 Humidity sensor. Available https:// www.dwyer-inst.com/Product/AirQuality/Humidity-TemperatureTransmitters/Model657-1#specs. Accessed 4 Feb 2020.
Easte S505. S505 Light sensor. Available https://www.eastel33.com/index.php/Product/product_con.html?id=10#. Accessed 6 Feb 2020.
Enercorp 310. Arisense 310 CO2 sensor. Available https://www.enercorp.com/enercorp-pdf/air-quality/48.pdf. Accessed 7 Feb 2020.
FDS-100 Soil sensor. FDS-100 Soil moisture sensor. Available https://www.bjgxhy.en.alibaba.com/?spm=a2700.details.cordpanyb.4.14a64a0dAYuVfj. Accessed 8 Feb 2020.
Funnykit H-550. H550 CO2 sensor. Available https://www.funnykit.co.kr/bemarket/datasheet/H-550.pdf. Accessed 7 Feb 2020.
Hamamatsu S1087. S1087-01 Light sensor. Available https://www.hamamatsu.com/resources/pdf/ssd/s1087_etc_kspd1039e.pdf. Accessed 6 Feb 2020.
Lutron. MCH-383SD Temperature, humidity, and CO2 data logger. Available https://www.lutron.com.tw/ugC_ShowroomItem_Detail.asp?hidKindID=1&hidTypeID=83&hidCatID=&hidShowID=1198&hidPrdType=&txtSrhData. Accessed 4 Feb 2020.
Memsic MDA300. MDA 300 wireless sensor board. Available https://www.memsic.com/userfiles/files/Datasheets/WSN/6020-0052-04_a_mda300-t.pdf. Accessed 2 Feb 2020.
Mouser BH1750. BH1750 Light sensor. Available https://www.mouser.com/ds/2/348/bh1750fvi-e-186247.pdf. Accessed 6 Feb 2020.
Mouser FC-22. FC-22 Soil moisture sensor. Available https://www.mouser.com/ds/2/744/Seeed_101020008-1217463.pdf. Accessed 7 Feb 2020.
Mouser MCP9700. MCP9700A Temperature sensor. Available https://www.mouser.com/catalog/specsheets/intersil_fn3171.pdf. Accessed 4 Feb 2020.
Mouser MQ5. MQ5 CO2 sensor. Available https://www.mouser.com/ds/2/744/Seeed_101020056-1217478.pdf. Accessed 7 Feb 2020.
Mouser SHT75. SHT75 humidity sensor. Available https:// www.mouser.com/ds/2/682/ Sensirion_Humidity_SHT7x_Datasheet_V5-469726.pdf. Accessed 3 Feb 2020.
Nxp MPX4115. MPX4115 Air pressure sensor. Available https://www.nxp.com/files-static/sensors/doc/data_sheet/MPX4115.pdf. Accessed 9 Feb 2020.
Sensirion humidity sensor. Digital humidity sensor. Available https://www.sensirion.com/en/environmental-sensors/humidity-sensors. Accessed 2 Feb 2020.
Sensirion SHT71. Digital humidity sensor. Available https://www.sensirion.com/en/environmental-sensors/humidity-sensors. Accessed 3 Feb 2020.
Soil PT100. PT100 Soil temperature and moisture sensor. Available https://www.soil.co.uk/products/temperature-sensors/pt100-resistance-temperature-sensor/. Accessed 8 Feb 2020.
TES 133R. 133R solar irradiance sensor. Available https://www.tes.com.tw/en/product_detail.asp?seq=285. Accessed 8 Feb 2020.
Ti LM35. LM35 Temperature sensor. Available https://www.ti.com/lit/ds/symlink/lm35.pdf. Accessed 4 Feb 2020.
TME 808. 808H5V5 Humidity sensor. Available https://www.tme.eu/en/details/sens-808h5v5/humidity-sensors. Accessed 4 Feb 2020.
TME HS220. HS220 Humidity sensor. Available https:// www.tme.eu/en/details/sy-hs-220/humidity-sensors/syhitech. Accessed 4 Feb 2020.
Zytemp TN9. TN9 Thermal camera. Available https://www.zytemp.com/products/files/TN9_UserManual_012.pdf. Accessed 9 Feb 2020.
Acknowledgments
We would like to thank Editage (www.editage.co.kr) for English language editing.
Funding
This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Agriculture, Food and Rural Affairs Convergence Technologies Program for Educating Creative Global Leader, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (717001-7).
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Bhujel, A., Basak, J.K., Khan, F. et al. Sensor Systems for Greenhouse Microclimate Monitoring and Control: a Review. J. Biosyst. Eng. 45, 341–361 (2020). https://doi.org/10.1007/s42853-020-00075-6
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DOI: https://doi.org/10.1007/s42853-020-00075-6