Sensor Systems for Greenhouse Microclimate Monitoring and Control: a Review

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....

Full description

Saved in:
Bibliographic Details
Published inJournal of Biosystems Engineering, 45(4) Vol. 45; no. 4; pp. 341 - 361
Main Authors Bhujel, Anil, Basak, Jayanta Kumar, Khan, Fawad, Arulmozhi, Elanchezhian, Jaihuni, Mustafa, Sihalath, Thavisack, Lee, Deoghyun, Park, Jaesung, Kim, Hyeon Tae
Format Journal Article
LanguageEnglish
Published Singapore Springer Singapore 01.12.2020
한국농업기계학회
Subjects
Agriculture
Control
Engineering
Environmental Engineering/Biotechnology
Mechatronics
Review
Robotics
농학
Soft sensing
WSN
Greenhouse microclimate
Microclimate monitoring
Sensor
Online AccessGet full text
ISSN1738-1266
2234-1862
DOI10.1007/s42853-020-00075-6

Cover

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.
AbstractList 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. KCI Citation Count: 0
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.
Author Park, Jaesung
Khan, Fawad
Sihalath, Thavisack
Kim, Hyeon Tae
Arulmozhi, Elanchezhian
Jaihuni, Mustafa
Bhujel, Anil
Lee, Deoghyun
Basak, Jayanta Kumar
Author_xml – sequence: 1
  givenname: Anil
  surname: Bhujel
  fullname: Bhujel, Anil
  organization: Department of Bio-systems Engineering, Institute of Smart Farm, Gyeongsang National University, Government of Nepal, Ministry of Communication and Information Technology
– sequence: 2
  givenname: Jayanta Kumar
  surname: Basak
  fullname: Basak, Jayanta Kumar
  organization: Department of Bio-systems Engineering, Institute of Smart Farm, Gyeongsang National University
– sequence: 3
  givenname: Fawad
  surname: Khan
  fullname: Khan, Fawad
  organization: Department of Bio-systems Engineering, Institute of Smart Farm, Gyeongsang National University
– sequence: 4
  givenname: Elanchezhian
  surname: Arulmozhi
  fullname: Arulmozhi, Elanchezhian
  organization: Department of Bio-systems Engineering, Institute of Smart Farm, Gyeongsang National University
– sequence: 5
  givenname: Mustafa
  surname: Jaihuni
  fullname: Jaihuni, Mustafa
  organization: Department of Bio-systems Engineering, Institute of Smart Farm, Gyeongsang National University
– sequence: 6
  givenname: Thavisack
  surname: Sihalath
  fullname: Sihalath, Thavisack
  organization: Department of Bio-systems Engineering, Institute of Smart Farm, Gyeongsang National University
– sequence: 7
  givenname: Deoghyun
  surname: Lee
  fullname: Lee, Deoghyun
  organization: Department of Bio-systems Engineering, Institute of Smart Farm, Gyeongsang National University
– sequence: 8
  givenname: Jaesung
  surname: Park
  fullname: Park, Jaesung
  organization: Smart Farm Research Center, Gyeongsang National University
– sequence: 9
  givenname: Hyeon Tae
  surname: Kim
  fullname: Kim, Hyeon Tae
  email: bioani@gnu.ac.kr
  organization: Department of Bio-systems Engineering, Institute of Smart Farm, Gyeongsang National University
BackLink https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002684802$$DAccess content in National Research Foundation of Korea (NRF)
BookMark eNp9kE1PAjEQhhujiYj8AU979bDaj6XteiNEkYgxATw3ZXeKhaUl7aLh31vAkwdOM5O8z2TmuUGXzjtA6I7gB4KxeIwFlX2WY4pznOZ-zi9Qh1JW5ERyeok6RDCZE8r5NerFuEohRoRkfd5BbzNw0Ydsto8tbGJmUj8KAO7L7yJk77YKvmrsRrdp8M62Pli3zLSrs6F3bfDNU6azKXxb-LlFV0Y3EXp_tYs-X57nw9d88jEaDweTvKIF5rk0XEqqMRima9AEwJC-EMAZLApZYkGKqsBQGkxrQkRpdF2T9OLCQEUAF6yL7k97XTBqXVnltT3WpVfroAbT-ViVoqCC85SVp2z6I8YARlW21a093K5towhWB4fq5FAlh-roUB1Q-g_dhiQi7M9D7ATF7UEUBLXyu-CSjnPUL0a_hbI
CitedBy_id crossref_primary_10_1016_j_compag_2022_107139
crossref_primary_10_1080_21642583_2024_2306825
crossref_primary_10_1016_j_scs_2022_104259
crossref_primary_10_3390_atmos13081258
crossref_primary_10_1016_j_apenergy_2023_121190
crossref_primary_10_3390_agriengineering6040267
crossref_primary_10_3390_app14146158
crossref_primary_10_3390_encyclopedia5010035
crossref_primary_10_3390_crops2040024
crossref_primary_10_1016_j_asej_2023_102509
crossref_primary_10_3390_agriculture12101705
crossref_primary_10_3390_su162410958
crossref_primary_10_1038_s41598_025_94432_0
crossref_primary_10_1016_j_sbsr_2024_100688
crossref_primary_10_2139_ssrn_4943130
crossref_primary_10_3390_heritage7110300
crossref_primary_10_3390_en15103834
crossref_primary_10_1016_j_atech_2024_100446
crossref_primary_10_3389_fhort_2024_1425285
crossref_primary_10_3390_math11143052
crossref_primary_10_3389_fpls_2022_920284
crossref_primary_10_1016_j_atech_2023_100296
crossref_primary_10_1016_j_compag_2024_108652
crossref_primary_10_3390_app122412557
crossref_primary_10_1016_j_measen_2023_100727
crossref_primary_10_3390_s23031250
crossref_primary_10_1016_j_inpa_2024_04_002
Cites_doi 10.1007/978-981-13-3338-5
10.1016/j.ifacol.2019.12.518
10.5120/16672-6673
10.1109/ICAMIMIA.2015.7507993
10.1016/j.procs.2019.09.001
10.1079/9781780641034.0000
10.1016/j.biosystemseng.2019.10.005
10.1016/j.procs.2017.11.042
10.3390/en13020435
10.1016/j.ifacol.2015.07.062
10.3389/fpls.2015.00704
10.3390/s18113786
10.1016/j.rser.2011.07.030
10.1016/j.jclepro.2017.02.197
10.3390/s20072078
10.1109/ICITBS.2015.126
10.23919/ChiCC.2017.8028786
10.1016/j.compchemeng.2006.05.030
10.1016/j.eaef.2014.02.003
10.1016/j.compag.2020.105287
10.3390/en12091749
10.1088/1757-899X/396/1/012027
10.1016/j.procs.2017.08.300
10.17979/spudc.9788497497565.0875
10.1002/9780470866931.ch6
10.1109/EBCCSP.2016.7605236
10.12785/ijcds/080210
10.1007/978-1-4302-6014-1_4
10.21273/HORTSCI12676-17
10.1007/s42853-019-00014-0
10.1016/j.inpa.2016.06.002
10.1080/03772063.2014.999834
10.1007/s00271-006-0034-z
10.1016/j.ifacol.2018.08.099
10.1016/j.compag.2018.11.014
10.1109/ICCAR.2017.7942739
10.1515/intag-2017-0005
10.1109/ISAMSR.2016.7810003
10.1016/j.jhydrol.2007.06.032
10.1016/j.compag.2017.03.003
10.3390/s20072028
10.1016/j.biosystemseng.2016.11.005
10.1109/SSD.2014.6808765
10.1109/ICUFN.2017.7993788
10.1109/ICCAR.2018.8384696
10.1016/j.procs.2015.08.249
10.1016/j.csi.2011.03.004
10.1109/IMCEC.2018.8469309
10.1109/VTCSpring.2017.8108182
10.1109/COMSNETS48256.2020.9027392
10.1016/j.inpa.2018.02.002
10.3390/en10070927
10.1109/ICARA.2015.7081156
10.5307/jbe.2017.42.1.023
10.1109/WiSPNET.2018.8538702
10.1016/j.compag.2019.104877
10.1109/CATCON.2017.8280184
10.1016/j.njas.2014.05.007
10.5296/npa.v10i4.14155
10.15666/aeer/1802_21412161
10.3303/CET1546124
10.1016/j.biosystemseng.2018.10.017
10.3389/fpls.2018.02002
10.1016/j.ifacol.2019.12.520
10.1016/j.icte.2017.12.005
10.1016/j.compag.2018.12.039
10.1016/j.inpa.2018.01.002
10.1007/s11947-008-0154-y
10.5593/sgem2017/62/S27.089
10.1016/j.crcon.2018.08.002
10.1109/IMPACT.2018.8625804
10.1016/j.ifacol.2016.10.068
10.1016/j.ifacol.2018.08.108
10.1016/j.icte.2017.03.004
10.5815/ijieeb.2017.03.01
10.1109/CYBER.2015.7288044
10.1016/j.inpa.2018.01.003
10.1109/ICSGEA.2017.47
10.1016/j.inpa.2018.01.001
10.1007/978-981-13-1882-5
10.1109/CSSE.2008.1528
10.1109/ICICT.2017.8320190
10.1016/j.eaef.2013.12.009
10.1109/ICAwST.2017.8256458
10.3390/s19010060
10.5307/JBE.2018.43.1.072
10.1109/ATC.2018.8587487
ContentType Journal Article
Copyright The Korean Society for Agricultural Machinery 2021
Copyright_xml – notice: The Korean Society for Agricultural Machinery 2021
DBID AAYXX
CITATION
ACYCR
DOI 10.1007/s42853-020-00075-6
DatabaseName CrossRef
Korean Citation Index
DatabaseTitle CrossRef
DatabaseTitleList

DeliveryMethod fulltext_linktorsrc
Discipline Engineering
Agriculture
EISSN 2234-1862
EndPage 361
ExternalDocumentID oai_kci_go_kr_ARTI_9742766
10_1007_s42853_020_00075_6
GrantInformation_xml – fundername: Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries
  grantid: 716001-7
  funderid: http://dx.doi.org/10.13039/501100003668
GroupedDBID .UV
0R~
406
9ZL
AACDK
AAHNG
AAJBT
AASML
AATNV
ABAKF
ABECU
ABKCH
ABMQK
ABTEG
ABTKH
ACAOD
ACDTI
ACHSB
ACOKC
ACPIV
ACZOJ
ADTPH
ADURQ
AEFQL
AEMSY
AESKC
AGMZJ
AGQEE
AIGIU
AILAN
AITGF
AJZVZ
ALMA_UNASSIGNED_HOLDINGS
AMXSW
AXYYD
DPUIP
EBLON
EBS
EJD
FIGPU
FNLPD
IKXTQ
IWAJR
JDI
JZLTJ
LLZTM
NPVJJ
NQJWS
OK1
PT4
ROL
RSV
SJYHP
SNE
SNPRN
SOHCF
SOJ
SRMVM
SSLCW
UOJIU
UTJUX
ZMTXR
AAYXX
ABBRH
ABDBE
ABFSG
ACSTC
AEZWR
AFDZB
AFHIU
AHPBZ
AHWEU
AIXLP
ATHPR
AYFIA
CITATION
AAUYE
ABFTV
ABTMW
ACYCR
ADKNI
ADYFF
AFQWF
AGDGC
AMKLP
AMYLF
GGCAI
ID FETCH-LOGICAL-c2406-8f6882a0ef3adea1eef1577e63eb4890714c40e9f02d1179fadd1428bfec1e043
ISSN 1738-1266
IngestDate Tue Nov 21 21:40:43 EST 2023
Tue Jul 01 03:07:59 EDT 2025
Thu Apr 24 22:56:46 EDT 2025
Fri Feb 21 02:49:20 EST 2025
IsPeerReviewed true
IsScholarly true
Issue 4
Keywords Soft sensing
WSN
Greenhouse microclimate
Microclimate monitoring
Sensor
Language English
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c2406-8f6882a0ef3adea1eef1577e63eb4890714c40e9f02d1179fadd1428bfec1e043
PageCount 21
ParticipantIDs nrf_kci_oai_kci_go_kr_ARTI_9742766
crossref_citationtrail_10_1007_s42853_020_00075_6
crossref_primary_10_1007_s42853_020_00075_6
springer_journals_10_1007_s42853_020_00075_6
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 20201200
2020-12-00
2020-12
PublicationDateYYYYMMDD 2020-12-01
PublicationDate_xml – month: 12
  year: 2020
  text: 20201200
PublicationDecade 2020
PublicationPlace Singapore
PublicationPlace_xml – name: Singapore
PublicationTitle Journal of Biosystems Engineering, 45(4)
PublicationTitleAbbrev J. Biosyst. Eng
PublicationYear 2020
Publisher Springer Singapore
한국농업기계학회
Publisher_xml – name: Springer Singapore
– name: 한국농업기계학회
References PrasadBVGChakravortySEffects of climate change on vegetable cultivation-a reviewNature Environment and Pollution Technology2015144923929
NelsonPVGreenhouse operation and management2003Upper Saddle River, NJPrentice Hall
Datasheet4u TGS4161. TGS 4161 CO2 sensor. Available https://www. datasheet4u.com/datasheet-pdf-file/842954/Figaro/TGS4161/1. Accessed 6 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.
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.
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.
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.
Adafruit DHT11. DHT11 basic temperature-humidity sensor. Available https://www.adafruit.com/product/386. Accessed 2 Feb 2020.
Huynh, T. (2015). Thermal sensors. In Smart Sensor Systems (pp. 5–42). Berlin: Springer. https://doi.org/10.1002/9780470866931.ch6.
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.
Mouser FC-22. FC-22 Soil moisture sensor. Available https://www.mouser.com/ds/2/744/Seeed_101020008-1217463.pdf. Accessed 7 Feb 2020.
BonachelaSGonzálezAMFernándezMDIrrigation scheduling of plastic greenhouse vegetable crops based on historical weather dataIrrigation Science2006251536210.1007/s00271-006-0034-z
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.
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.
Funnykit H-550. H550 CO2 sensor. Available https://www.funnykit.co.kr/bemarket/datasheet/H-550.pdf. Accessed 7 Feb 2020.
Enercorp 310. Arisense 310 CO2 sensor. Available https://www.enercorp.com/enercorp-pdf/air-quality/48.pdf. Accessed 7 Feb 2020.
TME HS220. HS220 Humidity sensor. Available https:// www.tme.eu/en/details/sy-hs-220/humidity-sensors/syhitech. Accessed 4 Feb 2020.
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.
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.
Adafruit DHT22. DHT22 temperature-humidity sensor + extras. Available https://www.adafruit.com/product/385. Accessed 2 Feb 2020.
Sensirion humidity sensor. Digital humidity sensor. Available https://www.sensirion.com/en/environmental-sensors/humidity-sensors. Accessed 2 Feb 2020.
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.
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.
Sensirion SHT71. Digital humidity sensor. Available https://www.sensirion.com/en/environmental-sensors/humidity-sensors. Accessed 3 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.
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.
BogenaHRHuismanJAOberdörsterCVereeckenHEvaluation of a low-cost soil water content sensor for wireless network applicationsJournal of Hydrology20073441–2324210.1016/j.jhydrol.2007.06.032
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.
TES 133R. 133R solar irradiance sensor. Available https://www.tes.com.tw/en/product_detail.asp?seq=285. Accessed 8 Feb 2020.
AhamedMSGuoHTaninoKEnergy saving techniques for reducing the heating cost of conventional greenhousesBiosystems Engineering201917893310.1016/j.biosystemseng.2018.10.017
DattaSTaghvaeianSOchsnerTEMoriasiDGowdaPSteinerJLPerformance assessment of five different soil moisture sensors under irrigated field conditions in OklahomaSensors (Switzerland)2018181111710.3390/s18113786
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.
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.
MartinovićGSimonJGreenhouse microclimatic environment controlled by a mobile measuring stationNJAS - Wageningen Journal of Life Sciences201470617010.1016/j.njas.2014.05.007
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.
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.
KaburuanERJayadiRHarisnoA design of IoT-based monitoring system for intelligence indoor micro-climate horticulture farming in IndonesiaProcedia Computer Science201915745946410.1016/j.procs.2019.09.001
Zytemp TN9. TN9 Thermal camera. Available https://www.zytemp.com/products/files/TN9_UserManual_012.pdf. Accessed 9 Feb 2020.
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.
SinhaRSWeiYHwangSHA survey on LPWA technology: LoRa and NB-IoTICT Express201731142110.1016/j.icte.2017.03.004
CodeluppiGCilfoneADavoliLFerrariGLoraFarM: a LoRaWAN-based smart farming modular IoT architectureSensors2020207202810.3390/s20072028
Nxp MPX4115. MPX4115 Air pressure sensor. Available https://www.nxp.com/files-static/sensors/doc/data_sheet/MPX4115.pdf. Accessed 9 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.
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.
TakiMAbdanan MehdizadehSRohaniARahnamaMRahmati-JoneidabadMApplied machine learning in greenhouse simulation; new application and analysisInformation Processing in Agriculture20185225326810.1016/j.inpa.2018.01.003
ChandrasekaranNSomanahRRughooDDreepaulRKTyagarajaSCundenMDemkahMDigital transformation from leveraging blockchain technology, artificial intelligence, machine learning and deep learningAdv. Intell. Syst. Comput.201986327128310.1007/978-981-13-3338-5
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.
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.
XuJDaiFXuYYaoCLiCWireless power supply technology for uniform magnetic field of intelligent greenhouse sensorsComputers and Electronics in Agriculture2019156April 201820320810.1016/j.compag.2018.11.014
ZhangYChenDWangSTianLA promising trend for field information collection: an air-ground multi-sensor monitoring systemInformation Processing in Agriculture20185222423310.1016/j.inpa.2018.02.002
WuYLiLLiMZhangMSunHSygrimisNLaiWRemote-control system for greenhouse based on open source hardwareIFAC-PapersOnLine2019523017818310.1016/j.ifacol.2019.12.518
Takeya, S., Muromachi, S., Maekawa, T., Yamamoto, Y., Mimachi, H., Kinoshita, T., Murayama, T., Umeda, H., Ahn, D. H., Iwasaki, Y., H
TC Jermin Jeaunita (75_CR36) 2019; 8
A Kochhar (75_CR42) 2019; 163
PV Nelson (75_CR65) 2003
Y Kaneda (75_CR39) 2015; 60
75_CR72
75_CR74
NL Panwar (75_CR68) 2011; 15
75_CR76
75_CR78
75_CR77
S Datta (75_CR22) 2018; 18
75_CR79
Liang-Ying (75_CR50) 2015
JD Borrero (75_CR14) 2020; 20
B Mohammadi (75_CR61) 2018; 5
K Mekki (75_CR60) 2019; 5
75_CR133
JS Wilson (75_CR93) 2005
75_CR132
75_CR135
75_CR134
G Martinović (75_CR58) 2014; 70
75_CR131
A Elanchezhian (75_CR23) 2020; 18
75_CR130
T Achouak (75_CR2) 2019; 10
75_CR81
75_CR80
75_CR83
75_CR85
75_CR84
MH Liang (75_CR51) 2018; 51
75_CR87
75_CR1
75_CR89
YC Chiu (75_CR20) 2014; 7
75_CR88
75_CR129
75_CR7
75_CR6
75_CR9
75_CR126
75_CR125
75_CR128
75_CR127
75_CR122
RS Sinha (75_CR82) 2017; 3
75_CR121
75_CR124
75_CR123
N Chandrasekaran (75_CR16) 2019; 863
75_CR120
A Pawlowski (75_CR69) 2016
G Codeluppi (75_CR21) 2020; 20
L Zhang (75_CR97) 2015; 46
SY Lee (75_CR48) 2019; 188
E Kaiser (75_CR38) 2019; 9
75_CR52
75_CR54
CH Guzmán (75_CR29) 2019; 19
75_CR56
75_CR55
75_CR57
M Taki (75_CR86) 2018; 5
75_CR119
75_CR59
75_CR118
75_CR115
ER Kaburuan (75_CR37) 2019; 157
T Li (75_CR49) 2015; 6
75_CR114
75_CR117
75_CR116
75_CR111
75_CR110
75_CR113
75_CR112
JY Kim (75_CR41) 2018; 43
Y Zhang (75_CR98) 2018; 5
Y Wu (75_CR94) 2019; 52
CK Lee (75_CR47) 2019; 12
75_CR63
75_CR62
75_CR64
75_CR67
75_CR66
G Aiello (75_CR4) 2018; 172
B Lin (75_CR53) 2007; 31
75_CR108
A Khanna (75_CR40) 2019; 157
75_CR107
75_CR109
75_CR104
75_CR103
75_CR106
75_CR105
75_CR100
JK Basak (75_CR11) 2019; 44
75_CR102
75_CR101
S Rodríguez (75_CR75) 2017; 121
J Xu (75_CR95) 2019; 156
MA Akkaş (75_CR5) 2017; 113
75_CR30
J Bao (75_CR10) 2018; 1
75_CR32
75_CR31
75_CR34
75_CR33
75_CR35
SHKV Lata (75_CR45) 2017
Aqeel-Ur-Rehman (75_CR8) 2014; 36
75_CR43
75_CR44
MS Ahamed (75_CR3) 2019; 178
IC Yang (75_CR96) 2014; 7
HR Bogena (75_CR12) 2007; 344
75_CR92
K Radha-Manohar (75_CR71) 2007
75_CR91
DR Vimla (75_CR90) 2019; 863
S Bonachela (75_CR13) 2006; 25
75_CR99
75_CR15
75_CR18
75_CR17
75_CR19
75_CR25
75_CR24
75_CR27
75_CR26
BVG Prasad (75_CR70) 2015; 14
75_CR28
M Lauridsen (75_CR46) 2017
JC Ramos-Fernández (75_CR73) 2016; 49
References_xml – reference: AielloGGiovinoIValloneMCataniaPArgentoAA decision support system based on multisensor data fusion for sustainable greenhouse managementJournal of Cleaner Production20181724057406510.1016/j.jclepro.2017.02.197
– reference: Funnykit H-550. H550 CO2 sensor. Available https://www.funnykit.co.kr/bemarket/datasheet/H-550.pdf. Accessed 7 Feb 2020.
– reference: 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.
– reference: 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.
– reference: 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.
– reference: 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.
– reference: KhannaAKaurSEvolution of Internet of Things (IoT) and its significant impact in the field of Precision AgricultureComputers and Electronics in Agriculture2019157November 201821823110.1016/j.compag.2018.12.039
– reference: 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.
– reference: ZhangYChenDWangSTianLA promising trend for field information collection: an air-ground multi-sensor monitoring systemInformation Processing in Agriculture20185222423310.1016/j.inpa.2018.02.002
– reference: LauridsenMNguyenHVejlgaardBKovacsIZMogensenPSorensenMCoverage comparison of GPRS, NB-IoT, LoRa, and SigFox in a 7800 km areaIEEE Vehicular Technology Conference, 2017-June20172610.1109/VTCSpring.2017.8108182
– reference: KanedaYIbayashiHOishiNMinenoHGreenhouse environmental control system based on SW-SVRProcedia Computer Science201560186086910.1016/j.procs.2015.08.249
– reference: Component LDR. Light Dependent Resistor. Available https://www.components101.com/ldr-datasheet. Accessed 5 Feb 2020.
– reference: BonachelaSGonzálezAMFernándezMDIrrigation scheduling of plastic greenhouse vegetable crops based on historical weather dataIrrigation Science2006251536210.1007/s00271-006-0034-z
– reference: ChiuYCYangPYGriftTEA wireless communication system for automated greenhouse operationsEngineering in Agriculture, Environment and Food201472788510.1016/j.eaef.2014.02.003
– reference: 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.
– reference: 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.
– reference: 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. 
– reference: KimJYSangcheol KimJLComparative analysis of TTAK.KO-06.0288-Part3 and development of an open-source communication library for greenhouse control systemJournal of Biosystems Engineering2018431728010.5307/JBE.2018.43.1.072
– reference: Mouser MCP9700. MCP9700A Temperature sensor. Available https://www.mouser.com/catalog/specsheets/intersil_fn3171.pdf. Accessed 4 Feb 2020.
– reference: 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.
– reference: WilsonJSSensor Technology Handbook2005Burlington, MA 01803, USAElsevier Inc.
– reference: BorreroJDZabaloAAn autonomous wireless device for real-time monitoring of water needsSensors (Switzerland)202020711610.3390/s20072078
– reference: 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.
– reference: 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.
– reference: XuJDaiFXuYYaoCLiCWireless power supply technology for uniform magnetic field of intelligent greenhouse sensorsComputers and Electronics in Agriculture2019156April 201820320810.1016/j.compag.2018.11.014
– reference: 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.
– reference: NelsonPVGreenhouse operation and management2003Upper Saddle River, NJPrentice Hall
– reference: MartinovićGSimonJGreenhouse microclimatic environment controlled by a mobile measuring stationNJAS - Wageningen Journal of Life Sciences201470617010.1016/j.njas.2014.05.007
– reference: 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.
– reference: RodríguezSGualotuñaTGriloCA system for the monitoring and predicting of data in precision agriculture in a rose greenhouse based on wireless sensor networksProcedia Computer Science201712130631310.1016/j.procs.2017.11.042
– reference: 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.
– reference: 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.
– reference: TES 133R. 133R solar irradiance sensor. Available https://www.tes.com.tw/en/product_detail.asp?seq=285. Accessed 8 Feb 2020.
– reference: Datasheets DS18B20. DS18B20 Soil temperature and moisture sensor. Available https://www.datasheets.maximintegrated.com/en/ds/DS18B20-PAR.pdf. Accessed 8 Feb 2020.
– reference: Dwyer 657. DWYER 657 Humidity sensor. Available https:// www.dwyer-inst.com/Product/AirQuality/Humidity-TemperatureTransmitters/Model657-1#specs. Accessed 4 Feb 2020.
– reference: Deltaohm 9009t. HD900TR humidity sensor. Available https://www.deltaohm.com/en/product/hd9008t-9009t-serie-meteorological-temperature-and-rh-transmitter. Accessed 3 Feb 2020.
– reference: 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.
– reference: TME 808. 808H5V5 Humidity sensor. Available https://www.tme.eu/en/details/sens-808h5v5/humidity-sensors. Accessed 4 Feb 2020.
– reference: Liang-YingGuoYFZhao-WeiGreenhouse environment monitoring system design based on WSN and GPRS networks2015 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 20152015ChinaShenyang79579810.1109/CYBER.2015.7288044
– reference: LiTYangQAdvantages of diffuse light for horticultural production and perspectives for further researchFrontiers in Plant Science20156september1510.3389/fpls.2015.00704
– reference: 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.
– reference: LeeSYLeeIBYeoUHKimRWKimJGOptimal sensor placement for monitoring and controlling greenhouse internal environmentsBiosystems Engineering201918819020610.1016/j.biosystemseng.2019.10.005
– reference: 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.
– reference: ElanchezhianABasakJKParkJKhanFOkyereFGLeeYEvaluating different models used for predicting the indoor microclimatic parameters of a greenhouseApplied Ecology and Environmental Research20201822141216110.15666/aeer/1802_21412161
– reference: DattaSTaghvaeianSOchsnerTEMoriasiDGowdaPSteinerJLPerformance assessment of five different soil moisture sensors under irrigated field conditions in OklahomaSensors (Switzerland)2018181111710.3390/s18113786
– reference: 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.
– reference: AchouakTKhelifaBGarcíaLParraLLloretJFatehBSensor network proposal for greenhouse automation placed at the south of AlgeriaNetwork Protocols and Algorithms20191045310.5296/npa.v10i4.14155
– reference: Bdtic 29010. ISL 29010 Light sensor. Available https://www.bdtic.com/en/intersil/ISL29010. Accessed 5 Feb 2020.
– reference: 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.
– reference: 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.
– reference: Mouser MQ5. MQ5 CO2 sensor. Available https://www.mouser.com/ds/2/744/Seeed_101020056-1217478.pdf. Accessed 7 Feb 2020.
– reference: 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.
– reference: PawlowskiASanchezJAGuzmanJLRodriguezFBerenguelMDormidoSEvent-based control for a greenhouse irrigation system2016 2nd International Conference on Event-Based Control, Communication, and Signal Processing, EBCCSP 2016 - Proceedings. Krakow, Poland201610.1109/EBCCSP.2016.7605236
– reference: Nxp MPX4115. MPX4115 Air pressure sensor. Available https://www.nxp.com/files-static/sensors/doc/data_sheet/MPX4115.pdf. Accessed 9 Feb 2020.
– reference: 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.
– reference: Mouser FC-22. FC-22 Soil moisture sensor. Available https://www.mouser.com/ds/2/744/Seeed_101020008-1217463.pdf. Accessed 7 Feb 2020.
– reference: 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.
– reference: ZhangLLiCJiaYXiaoZDesign of greenhouse environment remote monitoring system based on Android platformChemical Engineering Transactions20154673974410.3303/CET1546124
– reference: 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.
– reference: Datasheet4u TGS4161. TGS 4161 CO2 sensor. Available https://www. datasheet4u.com/datasheet-pdf-file/842954/Figaro/TGS4161/1. Accessed 6 Feb 2020.
– reference: 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.
– reference: 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.
– reference: 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.
– reference: Huynh, T. (2015). Thermal sensors. In Smart Sensor Systems (pp. 5–42). Berlin: Springer. https://doi.org/10.1002/9780470866931.ch6.
– reference: KochharAKumarNWireless sensor networks for greenhouses: an end-to-end reviewComputers and Electronics in Agriculture2019163June10487710.1016/j.compag.2019.104877
– reference: Easte S505. S505 Light sensor. Available https://www.eastel33.com/index.php/Product/product_con.html?id=10#. Accessed 6 Feb 2020.
– reference: GuzmánCHCarreraJLDuránHABerumenJOrtizAAGuiretteOAImplementation of virtual sensors for monitoring temperature in greenhouses using CFD and controlSensors20191916010.3390/s19010060
– reference: Hamamatsu S1087. S1087-01 Light sensor. Available https://www.hamamatsu.com/resources/pdf/ssd/s1087_etc_kspd1039e.pdf. Accessed 6 Feb 2020.
– reference: 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.
– reference: Aqeel-Ur-RehmanAbbasiAZIslamNShaikhZAA review of wireless sensors and networks’ applications in agricultureComputer Standards and Interfaces201436226327010.1016/j.csi.2011.03.004
– reference: BasakJKQasimWOkyereFGKhanFLeeJParkJKimHTRegression analysis to estimate morphology parameters of pepper plant in a controlled greenhouse systemJournal of Biosystems Engineering2019442576810.1007/s42853-019-00014-0
– reference: ChandrasekaranNSomanahRRughooDDreepaulRKTyagarajaSCundenMDemkahMDigital transformation from leveraging blockchain technology, artificial intelligence, machine learning and deep learningAdv. Intell. Syst. Comput.201986327128310.1007/978-981-13-3338-5
– reference: 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.
– reference: TME HS220. HS220 Humidity sensor. Available https:// www.tme.eu/en/details/sy-hs-220/humidity-sensors/syhitech. Accessed 4 Feb 2020.
– reference: BogenaHRHuismanJAOberdörsterCVereeckenHEvaluation of a low-cost soil water content sensor for wireless network applicationsJournal of Hydrology20073441–2324210.1016/j.jhydrol.2007.06.032
– reference: CodeluppiGCilfoneADavoliLFerrariGLoraFarM: a LoRaWAN-based smart farming modular IoT architectureSensors2020207202810.3390/s20072028
– reference: 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.
– reference: Zytemp TN9. TN9 Thermal camera. Available https://www.zytemp.com/products/files/TN9_UserManual_012.pdf. Accessed 9 Feb 2020.
– reference: 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.
– reference: Radha-ManoharKIgathinathaneCGreenhouse technology and management20072HyderabadBS Publications10.1079/9781780641034.0000
– reference: Adafruit AM2302. AM2302 (Wired DHT22) temperature-humidity sensor. Available https://www.adafruit.com/product/393. Accessed 2 Feb 2020.
– reference: Mouser SHT75. SHT75 humidity sensor. Available https:// www.mouser.com/ds/2/682/ Sensirion_Humidity_SHT7x_Datasheet_V5-469726.pdf. Accessed 3 Feb 2020.
– reference: MekkiKBajicEChaxelFMeyerFA comparative study of LPWAN technologies for large-scale IoT deploymentICT Express2019511710.1016/j.icte.2017.12.005
– reference: Enercorp 310. Arisense 310 CO2 sensor. Available https://www.enercorp.com/enercorp-pdf/air-quality/48.pdf. Accessed 7 Feb 2020.
– reference: 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.
– reference: FAO. (2018). The future of food and agriculture – alternative pathways to 2050. http://www.fao.org/3/I8429EN/i8429en.pdf. Accessed 12 Jan 2020.
– reference: 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.
– reference: 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.
– reference: 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.
– reference: Aosong AM2301. AM2301A temperature and humidity sensor module. Available https://www.aosong.com/products-28.html. Accessed 3 Feb 2020.
– reference: 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.
– reference: LiangMHHeYFChenLJDuSFGreenhouse environment dynamic monitoring system based on WIFIIFAC-PapersOnLine2018511773674010.1016/j.ifacol.2018.08.108
– reference: 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.
– reference: 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.
– reference: YangICHsiehKWTsaiCYHuangYIChenYLChenSDevelopment of an automation system for greenhouse seedling production management using radio-frequency-identification and local remote sensing techniquesEngineering in Agriculture, Environment and Food201471525810.1016/j.eaef.2013.12.009
– reference: 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.
– reference: 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.
– reference: TakiMAbdanan MehdizadehSRohaniARahnamaMRahmati-JoneidabadMApplied machine learning in greenhouse simulation; new application and analysisInformation Processing in Agriculture20185225326810.1016/j.inpa.2018.01.003
– reference: Ti LM35. LM35 Temperature sensor. Available https://www.ti.com/lit/ds/symlink/lm35.pdf. Accessed 4 Feb 2020.
– reference: 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.
– reference: 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.
– reference: PrasadBVGChakravortySEffects of climate change on vegetable cultivation-a reviewNature Environment and Pollution Technology2015144923929
– reference: Sensirion humidity sensor. Digital humidity sensor. Available https://www.sensirion.com/en/environmental-sensors/humidity-sensors. Accessed 2 Feb 2020.
– reference: AhamedMSGuoHTaninoKEnergy saving techniques for reducing the heating cost of conventional greenhousesBiosystems Engineering201917893310.1016/j.biosystemseng.2018.10.017
– reference: BaoJLuW-HZhaoJBiXTGreenhouses for CO2 sequestration from atmosphereCarbon Resources Conversion20181218319010.1016/j.crcon.2018.08.002
– reference: AkkaşMASokulluRAn IoT-based greenhouse monitoring system with Micaz motesProcedia Computer Science201711360360810.1016/j.procs.2017.08.300
– reference: MohammadiBRanjbarSFAjabshirchiYApplication of dynamic model to predict some inside environment variables in a semi-solar greenhouseInformation Processing in Agriculture20185227928810.1016/j.inpa.2018.01.001
– reference: SinhaRSWeiYHwangSHA survey on LPWA technology: LoRa and NB-IoTICT Express201731142110.1016/j.icte.2017.03.004
– reference: Apogeeinstruments SQ-110. SQ-110 Solar irradiance sensor. Available https://www.apogeeinstruments.com/sq-110-ss-sun-calibration-quantum-sensor/. Accessed 9 Feb 2020.
– reference: 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.
– reference: 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.
– reference: LataSHKVSelection of sensor number and locations in intelligent greenhouse2017 3rd International Conference on Condition Assessment Techniques in Electrical Systems (CATCON)2017IndiaRupnagar510.1109/CATCON.2017.8280184
– reference: LeeCKChungMShinK-YImY-HYoonS-WA study of the effects of enhanced uniformity control of greenhouse environment variables on crop growthEnergies201912174910.3390/en12091749
– reference: Adafruit DHT22. DHT22 temperature-humidity sensor + extras. Available https://www.adafruit.com/product/385. Accessed 2 Feb 2020.
– reference: 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.
– reference: 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.
– reference: KaiserEOuzounisTGidayHSchipperRHeuvelinkEMarcelisLFMAdding blue to red supplemental light increases biomass and yield of greenhouse-grown tomatoes, but only to an optimumFrontiers in Plant Science20199January11110.3389/fpls.2018.02002
– reference: 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.
– reference: Jermin JeaunitaTCSarasvathiVFault tolerant sensor node placement for IoT based large scale automated greenhouse systemInternational Journal of Computing and Digital Systems20198218919710.12785/ijcds/080210
– reference: VimlaDRKhedoKKBhoyrooVA flexible and reliable wireless sensor network architecture for precision agriculture in a tomato greenhouseAdv. Intell. Syst. Comput.201986327128310.1007/978-981-13-3338-5
– reference: LinBReckeBKnudsenJKHJørgensenSBA systematic approach for soft sensor developmentComputers and Chemical Engineering2007315–641942510.1016/j.compchemeng.2006.05.030
– reference: Adafruit DHT11. DHT11 basic temperature-humidity sensor. Available https://www.adafruit.com/product/386. Accessed 2 Feb 2020.
– reference: 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.
– reference: 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.
– reference: 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.
– reference: Mouser BH1750. BH1750 Light sensor. Available https://www.mouser.com/ds/2/348/bh1750fvi-e-186247.pdf. Accessed 6 Feb 2020.
– reference: 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.
– reference: 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.
– reference: PanwarNLKaushikSCKothariSSolar greenhouse an option for renewable and sustainable farmingRenewable and Sustainable Energy Reviews20111583934394510.1016/j.rser.2011.07.030
– reference: Ramos-FernándezJCBalmatJFMárquez-VeraMALafontFPesselNEspinoza-QuesadaESFuzzy modeling vapor pressure deficit to monitoring microclimate in greenhousesIFAC-PapersOnLine2016491637137410.1016/j.ifacol.2016.10.068
– reference: Sensirion SHT71. Digital humidity sensor. Available https://www.sensirion.com/en/environmental-sensors/humidity-sensors. Accessed 3 Feb 2020.
– reference: 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.
– reference: WuYLiLLiMZhangMSunHSygrimisNLaiWRemote-control system for greenhouse based on open source hardwareIFAC-PapersOnLine2019523017818310.1016/j.ifacol.2019.12.518
– reference: KaburuanERJayadiRHarisnoA design of IoT-based monitoring system for intelligence indoor micro-climate horticulture farming in IndonesiaProcedia Computer Science201915745946410.1016/j.procs.2019.09.001
– reference: 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.
– volume: 863
  start-page: 271
  year: 2019
  ident: 75_CR90
  publication-title: Adv. Intell. Syst. Comput.
  doi: 10.1007/978-981-13-3338-5
– volume: 52
  start-page: 178
  issue: 30
  year: 2019
  ident: 75_CR94
  publication-title: IFAC-PapersOnLine
  doi: 10.1016/j.ifacol.2019.12.518
– ident: 75_CR76
  doi: 10.5120/16672-6673
– ident: 75_CR1
  doi: 10.1109/ICAMIMIA.2015.7507993
– volume: 157
  start-page: 459
  year: 2019
  ident: 75_CR37
  publication-title: Procedia Computer Science
  doi: 10.1016/j.procs.2019.09.001
– ident: 75_CR104
– volume-title: Greenhouse technology and management
  year: 2007
  ident: 75_CR71
  doi: 10.1079/9781780641034.0000
– volume: 188
  start-page: 190
  year: 2019
  ident: 75_CR48
  publication-title: Biosystems Engineering
  doi: 10.1016/j.biosystemseng.2019.10.005
– volume: 121
  start-page: 306
  year: 2017
  ident: 75_CR75
  publication-title: Procedia Computer Science
  doi: 10.1016/j.procs.2017.11.042
– ident: 75_CR127
– ident: 75_CR35
  doi: 10.3390/en13020435
– ident: 75_CR9
  doi: 10.1016/j.ifacol.2015.07.062
– ident: 75_CR44
– volume: 6
  start-page: 1
  issue: september
  year: 2015
  ident: 75_CR49
  publication-title: Frontiers in Plant Science
  doi: 10.3389/fpls.2015.00704
– volume: 18
  start-page: 1
  issue: 11
  year: 2018
  ident: 75_CR22
  publication-title: Sensors (Switzerland)
  doi: 10.3390/s18113786
– volume: 15
  start-page: 3934
  issue: 8
  year: 2011
  ident: 75_CR68
  publication-title: Renewable and Sustainable Energy Reviews
  doi: 10.1016/j.rser.2011.07.030
– volume: 172
  start-page: 4057
  year: 2018
  ident: 75_CR4
  publication-title: Journal of Cleaner Production
  doi: 10.1016/j.jclepro.2017.02.197
– ident: 75_CR133
– volume: 20
  start-page: 1
  issue: 7
  year: 2020
  ident: 75_CR14
  publication-title: Sensors (Switzerland)
  doi: 10.3390/s20072078
– ident: 75_CR56
  doi: 10.1109/ICITBS.2015.126
– ident: 75_CR110
– ident: 75_CR118
– ident: 75_CR124
– ident: 75_CR18
  doi: 10.23919/ChiCC.2017.8028786
– volume: 31
  start-page: 419
  issue: 5–6
  year: 2007
  ident: 75_CR53
  publication-title: Computers and Chemical Engineering
  doi: 10.1016/j.compchemeng.2006.05.030
– volume: 7
  start-page: 78
  issue: 2
  year: 2014
  ident: 75_CR20
  publication-title: Engineering in Agriculture, Environment and Food
  doi: 10.1016/j.eaef.2014.02.003
– ident: 75_CR72
  doi: 10.1016/j.compag.2020.105287
– volume: 12
  start-page: 1749
  year: 2019
  ident: 75_CR47
  publication-title: Energies
  doi: 10.3390/en12091749
– ident: 75_CR15
  doi: 10.1088/1757-899X/396/1/012027
– ident: 75_CR107
– volume: 113
  start-page: 603
  year: 2017
  ident: 75_CR5
  publication-title: Procedia Computer Science
  doi: 10.1016/j.procs.2017.08.300
– volume: 863
  start-page: 271
  year: 2019
  ident: 75_CR16
  publication-title: Adv. Intell. Syst. Comput.
  doi: 10.1007/978-981-13-3338-5
– ident: 75_CR113
– ident: 75_CR28
  doi: 10.17979/spudc.9788497497565.0875
– ident: 75_CR33
  doi: 10.1002/9780470866931.ch6
– volume-title: 2016 2nd International Conference on Event-Based Control, Communication, and Signal Processing, EBCCSP 2016 - Proceedings. Krakow, Poland
  year: 2016
  ident: 75_CR69
  doi: 10.1109/EBCCSP.2016.7605236
– ident: 75_CR78
– volume: 8
  start-page: 189
  issue: 2
  year: 2019
  ident: 75_CR36
  publication-title: International Journal of Computing and Digital Systems
  doi: 10.12785/ijcds/080210
– ident: 75_CR59
  doi: 10.1007/978-1-4302-6014-1_4
– volume-title: Sensor Technology Handbook
  year: 2005
  ident: 75_CR93
– ident: 75_CR31
  doi: 10.21273/HORTSCI12676-17
– ident: 75_CR130
– volume: 44
  start-page: 57
  issue: 2
  year: 2019
  ident: 75_CR11
  publication-title: Journal of Biosystems Engineering
  doi: 10.1007/s42853-019-00014-0
– ident: 75_CR87
  doi: 10.1016/j.inpa.2016.06.002
– ident: 75_CR83
  doi: 10.1080/03772063.2014.999834
– ident: 75_CR125
– volume: 25
  start-page: 53
  issue: 1
  year: 2006
  ident: 75_CR13
  publication-title: Irrigation Science
  doi: 10.1007/s00271-006-0034-z
– ident: 75_CR102
– ident: 75_CR32
  doi: 10.1016/j.ifacol.2018.08.099
– volume: 156
  start-page: 203
  issue: April 2018
  year: 2019
  ident: 75_CR95
  publication-title: Computers and Electronics in Agriculture
  doi: 10.1016/j.compag.2018.11.014
– ident: 75_CR55
  doi: 10.1109/ICCAR.2017.7942739
– ident: 75_CR79
  doi: 10.1515/intag-2017-0005
– ident: 75_CR34
  doi: 10.1109/ISAMSR.2016.7810003
– volume: 344
  start-page: 32
  issue: 1–2
  year: 2007
  ident: 75_CR12
  publication-title: Journal of Hydrology
  doi: 10.1016/j.jhydrol.2007.06.032
– ident: 75_CR52
  doi: 10.1016/j.compag.2017.03.003
– volume: 20
  start-page: 2028
  issue: 7
  year: 2020
  ident: 75_CR21
  publication-title: Sensors
  doi: 10.3390/s20072028
– ident: 75_CR131
– ident: 75_CR116
– ident: 75_CR122
– ident: 75_CR26
  doi: 10.1016/j.biosystemseng.2016.11.005
– ident: 75_CR6
  doi: 10.1109/SSD.2014.6808765
– ident: 75_CR66
  doi: 10.1109/ICUFN.2017.7993788
– ident: 75_CR54
  doi: 10.1109/ICCAR.2018.8384696
– volume: 60
  start-page: 860
  issue: 1
  year: 2015
  ident: 75_CR39
  publication-title: Procedia Computer Science
  doi: 10.1016/j.procs.2015.08.249
– ident: 75_CR105
– volume: 36
  start-page: 263
  issue: 2
  year: 2014
  ident: 75_CR8
  publication-title: Computer Standards and Interfaces
  doi: 10.1016/j.csi.2011.03.004
– ident: 75_CR99
  doi: 10.1109/IMCEC.2018.8469309
– start-page: 2
  volume-title: IEEE Vehicular Technology Conference, 2017-June
  year: 2017
  ident: 75_CR46
  doi: 10.1109/VTCSpring.2017.8108182
– ident: 75_CR81
  doi: 10.1109/COMSNETS48256.2020.9027392
– ident: 75_CR111
– volume: 5
  start-page: 224
  issue: 2
  year: 2018
  ident: 75_CR98
  publication-title: Information Processing in Agriculture
  doi: 10.1016/j.inpa.2018.02.002
– ident: 75_CR85
  doi: 10.3390/en10070927
– ident: 75_CR24
  doi: 10.1109/ICARA.2015.7081156
– ident: 75_CR119
– ident: 75_CR67
  doi: 10.5307/jbe.2017.42.1.023
– ident: 75_CR84
  doi: 10.1109/WiSPNET.2018.8538702
– volume: 163
  start-page: 104877
  issue: June
  year: 2019
  ident: 75_CR42
  publication-title: Computers and Electronics in Agriculture
  doi: 10.1016/j.compag.2019.104877
– ident: 75_CR100
– start-page: 5
  volume-title: 2017 3rd International Conference on Condition Assessment Techniques in Electrical Systems (CATCON)
  year: 2017
  ident: 75_CR45
  doi: 10.1109/CATCON.2017.8280184
– volume: 70
  start-page: 61
  year: 2014
  ident: 75_CR58
  publication-title: NJAS - Wageningen Journal of Life Sciences
  doi: 10.1016/j.njas.2014.05.007
– ident: 75_CR123
– volume: 10
  start-page: 53
  issue: 4
  year: 2019
  ident: 75_CR2
  publication-title: Network Protocols and Algorithms
  doi: 10.5296/npa.v10i4.14155
– volume: 18
  start-page: 2141
  issue: 2
  year: 2020
  ident: 75_CR23
  publication-title: Applied Ecology and Environmental Research
  doi: 10.15666/aeer/1802_21412161
– volume: 46
  start-page: 739
  year: 2015
  ident: 75_CR97
  publication-title: Chemical Engineering Transactions
  doi: 10.3303/CET1546124
– volume: 178
  start-page: 9
  year: 2019
  ident: 75_CR3
  publication-title: Biosystems Engineering
  doi: 10.1016/j.biosystemseng.2018.10.017
– ident: 75_CR25
– volume: 9
  start-page: 1
  issue: January
  year: 2019
  ident: 75_CR38
  publication-title: Frontiers in Plant Science
  doi: 10.3389/fpls.2018.02002
– ident: 75_CR63
  doi: 10.1016/j.ifacol.2019.12.520
– volume: 5
  start-page: 1
  issue: 1
  year: 2019
  ident: 75_CR60
  publication-title: ICT Express
  doi: 10.1016/j.icte.2017.12.005
– ident: 75_CR108
– volume-title: Greenhouse operation and management
  year: 2003
  ident: 75_CR65
– ident: 75_CR114
– volume: 157
  start-page: 218
  issue: November 2018
  year: 2019
  ident: 75_CR40
  publication-title: Computers and Electronics in Agriculture
  doi: 10.1016/j.compag.2018.12.039
– ident: 75_CR92
  doi: 10.1016/j.inpa.2018.01.002
– ident: 75_CR89
– ident: 75_CR64
  doi: 10.1007/s11947-008-0154-y
– ident: 75_CR62
  doi: 10.5593/sgem2017/62/S27.089
– ident: 75_CR103
– volume: 1
  start-page: 183
  issue: 2
  year: 2018
  ident: 75_CR10
  publication-title: Carbon Resources Conversion
  doi: 10.1016/j.crcon.2018.08.002
– ident: 75_CR120
– ident: 75_CR128
– ident: 75_CR17
  doi: 10.1109/IMPACT.2018.8625804
– ident: 75_CR43
– volume: 49
  start-page: 371
  issue: 16
  year: 2016
  ident: 75_CR73
  publication-title: IFAC-PapersOnLine
  doi: 10.1016/j.ifacol.2016.10.068
– volume: 51
  start-page: 736
  issue: 17
  year: 2018
  ident: 75_CR51
  publication-title: IFAC-PapersOnLine
  doi: 10.1016/j.ifacol.2018.08.108
– ident: 75_CR134
– volume: 3
  start-page: 14
  issue: 1
  year: 2017
  ident: 75_CR82
  publication-title: ICT Express
  doi: 10.1016/j.icte.2017.03.004
– ident: 75_CR57
– ident: 75_CR117
– ident: 75_CR77
  doi: 10.5815/ijieeb.2017.03.01
– ident: 75_CR88
– start-page: 795
  volume-title: 2015 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015
  year: 2015
  ident: 75_CR50
  doi: 10.1109/CYBER.2015.7288044
– volume: 5
  start-page: 253
  issue: 2
  year: 2018
  ident: 75_CR86
  publication-title: Information Processing in Agriculture
  doi: 10.1016/j.inpa.2018.01.003
– ident: 75_CR121
– ident: 75_CR106
– ident: 75_CR129
– ident: 75_CR30
  doi: 10.1109/ICSGEA.2017.47
– ident: 75_CR27
– ident: 75_CR112
– ident: 75_CR135
– volume: 5
  start-page: 279
  issue: 2
  year: 2018
  ident: 75_CR61
  publication-title: Information Processing in Agriculture
  doi: 10.1016/j.inpa.2018.01.001
– volume: 14
  start-page: 923
  issue: 4
  year: 2015
  ident: 75_CR70
  publication-title: Nature Environment and Pollution Technology
– ident: 75_CR7
  doi: 10.1007/978-981-13-1882-5
– ident: 75_CR126
– ident: 75_CR74
  doi: 10.1109/CSSE.2008.1528
– ident: 75_CR80
  doi: 10.1109/ICICT.2017.8320190
– ident: 75_CR101
– volume: 7
  start-page: 52
  issue: 1
  year: 2014
  ident: 75_CR96
  publication-title: Engineering in Agriculture, Environment and Food
  doi: 10.1016/j.eaef.2013.12.009
– ident: 75_CR19
  doi: 10.1109/ICAwST.2017.8256458
– volume: 19
  start-page: 60
  issue: 1
  year: 2019
  ident: 75_CR29
  publication-title: Sensors
  doi: 10.3390/s19010060
– volume: 43
  start-page: 72
  issue: 1
  year: 2018
  ident: 75_CR41
  publication-title: Journal of Biosystems Engineering
  doi: 10.5307/JBE.2018.43.1.072
– ident: 75_CR109
– ident: 75_CR115
– ident: 75_CR91
  doi: 10.1109/ATC.2018.8587487
– ident: 75_CR132
SSID ssj0003178356
ssib035520942
ssib007138302
ssib016108101
ssib001535042
Score 2.3276324
SecondaryResourceType review_article
Snippet Purpose Sensors are the primary component of a monitoring and control system. Effective monitoring and control of the microclimatic environment in a greenhouse...
Purpose Sensors are the primary component of a monitoring and control system. Effective monitoring and control of the microclimatic environment in a greenhouse...
SourceID nrf
crossref
springer
SourceType Open Website
Enrichment Source
Index Database
Publisher
StartPage 341
SubjectTerms Agriculture
Control
Engineering
Environmental Engineering/Biotechnology
Mechatronics
Review
Robotics
농학
Title Sensor Systems for Greenhouse Microclimate Monitoring and Control: a Review
URI https://link.springer.com/article/10.1007/s42853-020-00075-6
https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002684802
Volume 45
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
ispartofPNX Journal of Biosystems Engineering, 2020, 45(4), 187, pp.341-361
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bb9MwFLa67gUeEFcxbrIQbyUoFyfpeGunVQPEXrZJe7PsxFm7lgT1IsT-Cf-Wz5dctsLEeEkby7Xani_H57PP-UzIO4Ak0jJXXiGY8JgMck-CdXh-nobKz5M4Nsf5fD1Ojs7Y5_P4vNf71cla2qzlh-zqj3Ul_2NVtMGuukr2DpZtBkUD3sO-uMLCuP6TjU_AQatlrTpuMgZNHs0UdF7pjHjMTosZYtL62W1KEg9shrotde7sD2yHqXJWrdz4qtUubFj8dHPp9vnLNlljLFZibnNwf8JyYmASuRvfPrWLrhPxQ-Qt4DaLb9WVOWJ4cLgAFqcKdw67blkivJniUS9LDk70yd6gEqrjZFM42SBMnAS2aUOUwrxgeN0zW6FJh0DWcbORFctyM3Zk5dy3JgOb_7ECwYr1XrWpoE9j74bydkOFjHiz6czRmZvOPNkhuyEYiN8nu6PJeHxcOyvEaSGocdgs6CEQQzRrqtnq3-eKtEyp5ta3uBYI7ZTLYmsv3oQ4pw_JA2d0OrJAe0R6qnxM7o8ulk6fReGuo1_5hHyxAKQOgBQApC0AaReAtAUgBQCpA-BHKqiF31NyNjk8PTjy3OkcXqajQG9YJGBnwldFJHIlAqWKIE5TlURKsuG-LozLmK_2Cz_Mte5ggZlUy_vJQmWB8ln0jPTLqlTPCZVSYCrQSn4qY8yH2_CFCFkigkDGImF7JKj_Kp456Xp9gsqC_91ue2TQfOa7FW65tfdbWIDPsxnXeuv69aLi8yUHq_zEwbnDNEGn97WBuPMCq1vGfHG37i_JvfY5ekX66-VGvUbAu5ZvHPR-A4cDo1M
linkProvider Library Specific Holdings
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Sensor+Systems+for+Greenhouse+Microclimate+Monitoring+and+Control%3A+a+Review&rft.jtitle=Journal+of+biosystems+engineering&rft.au=Bhujel%2C+Anil&rft.au=Basak%2C+Jayanta+Kumar&rft.au=Khan%2C+Fawad&rft.au=Arulmozhi%2C+Elanchezhian&rft.date=2020-12-01&rft.pub=Springer+Singapore&rft.issn=1738-1266&rft.eissn=2234-1862&rft.volume=45&rft.issue=4&rft.spage=341&rft.epage=361&rft_id=info:doi/10.1007%2Fs42853-020-00075-6&rft.externalDocID=10_1007_s42853_020_00075_6
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1738-1266&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1738-1266&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1738-1266&client=summon