In view of the increasing material needs of the people and the lack of resources, agricultural greenhouses can efficiently provide a variety of crop products to meet the needs of contemporary society. This research mainly discusses the research of intelligent agricultural greenhouse monitoring system based on cloud computing. This system adopts the B/S structure, combined with the multi-core technology of modern browsers, the universal browser can realize the various needs of customers for the system, which greatly saves the cost of the project. DTH11 temperature and humidity sensor detects data from the temperature and humidity sensor drive module, collects temperature and humidity data for it through the bus, and outputs the collected temperature and humidity data in parallel. The cloud platform database uses the lower computer temperature and humidity sensor module to automatically monitor the temperature and humidity dynamic values of the agricultural greenhouse, and display it on the 12864 LCD panel in the greenhouse in real time; the data information displayed on the data screen in the greenhouse is transmitted to the LABVIEW host computer display interface ; For the air and soil temperature and humidity in the greenhouse, the cloud platform database management program can record correspondingly according to the day; the management program can query the ambient temperature and humidity information according to the day or month; the management program can set the warning temperature and prompt the greenhouse in time differences in the external environment; the management program can generate reports, output the reports in accordance with the relevant requirements of greenhouse management, and print the temperature record curve and data. The error is analyzed based on the test results of the upper computer interface of the PC cloud platform. The difference between the tested soil temperature and the actual temperature is about 1 degree, and the error of the light intensity value is about 3%. This research provides a reliable solution for the artificial cultivation of crops with large environmental gaps.

Asian Ullaha. Intelligent Agricultural Greenhouse Monitoring System Based on Cloud Computing. International Journal of Multimedia Computing  (2022), Vol. 3, Issue 3: 63-82. https://doi.org/10.38007/IJMC.2022.030305.

[1] He Q, H Zheng, Ma X, et al. Optical Analysis of A Sliding-Type Cylindrical Fresnel Lens Concentrating Collector for Agricultural Greenhouse. Journal of Daylighting, 2021, 8(1):110-119. DOI:10.15627/jd.2021.8

[2] He Y, Lan X, Zhou Z, et al. Analyzing the spatial network structure of agricultural greenhouse gases in China. Environmental Science and Pollution Research, 2021, 28(3-4):1-16.

[3] Li N, Shang L, Yu Z, et al. Estimation of agricultural greenhouse gases emission in interprovincial regions of China during 1996–2014. Natural Hazards, 2020, 100(3):1037-1058.

[4] Subhakala S, Muthulakshmi S, Geetha A, et al. Design of smart village using internet of things and cloud computing. Pakistan Journal of Biotechnology, 2017, 14(3):511-513.

[5] Indoria D, Indoria D. Interrelation between Cloud Computing Technology and Agriculture Fields. International Journal of Current Microbiology and Applied Sciences, 2019, 8(1):2991-2999. DOI:10.20546/ijcmas.2019.801.318

[6] Jukan A, Masip-Bruin X, Amla N. Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review. Acm Computing Surveys, 2017, 50(1):10.1-10.27. DOI:10.1145/3041960

[7] Defourny P, Bontemps S, Bellemans N, et al. Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world. Remote Sensing of Environment, 2018, 221 (2019):551–568. DOI:10.1016/j.rse.2018.11.007

[8] Koskinen J, Leinonen U, Vollrath A, et al. Participatory mapping of forest plantations with Open Foris and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 148(FEB.):63-74. DOI:10.1016/j.isprsjprs.2018.12.011

[9] Xia Z, Wang X, Zhang L, et al. A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing. IEEE Transactions on Information Forensics & Security, 2017, 11(11):2594-2608. DOI:10.1109/TIFS.2016.2590944

[10] Abbas H, Maennel O, Assar S. Security and privacy issues in cloud computing. Annals of Telecommunications, 2017, 72(5-6):233-235. DOI:10.14257/ijgdc.2014.7.2.09

[11] Deng R, Lu R, Lai C, et al. Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption. IEEE Internet of Things Journal, 2017, 3(6):1171-1181.

[12] Jin L, Zhang Y, Chen X, et al. Secure attribute-based data sharing for resource-limited users in cloud computing. Computers & Security, 2018, 72(JAN.):1-12. DOI:10.1016/j.cose.2017.08.007

[13] Wang Y, Li J, Wang H H. Cluster and cloud computing framework for scientific metrology in flow control. Cluster Computing, 2017, 22(1):1-10.

[14] Soofi A A, Khan M I. A Review on Data Security in Cloud Computing. International Journal of Computer Applications, 2017, 96(2):95-96. DOI:10.5120/16870-6767

[15] Bruin B D, Floridi L. The Ethics of Cloud Computing. Science and Engineering Ethics, 2017, 23(1):21-39. DOI:10.1007/s11948-016-9759-0

[16] Hirai T, Masuyyama H, Kasahara S, et al. Performance analysis of large-scale parallel-distributed processing with backup tasks for cloud computing. Journal of Industrial & Management Optimization, 2017, 10(1):113-129. DOI:10.1109/CCGRID.2009.40

[17] Sudarsan V, Satyanarayana N. Secure and Practical Outsourcing of Linear Programming in Cloud Computing: A Survey. International Journal of Computer Applications, 2017, 159(4):1-4. DOI:10.5120/ijca2017912903

[18] Wei W, Fan X, Song H, et al. Imperfect Information Dynamic Stackelberg Game Based Resource Allocation Using Hidden Markov for Cloud Computing. IEEE Transactions on Services Computing, 2018, 11(99):78-89.DOI:10.1109/TSC.2016.2528246

[19] Wang S, Zhou J, Member, et al. An Efficient File Hierarchy Attribute-Based Encryption Scheme in Cloud Computing. IEEE Transactions on Information Forensics and Security, 2017, 11(6):1265-1277. DOI:10.1109/TIFS.2016.2523941

[20] Barsoum A, Hasan M A. Provable Multicopy Dynamic Data Possession in Cloud Computing Systems. IEEE Transactions on Information Forensics & Security, 2017, 10(3):485-497.

[21] Fguiri A, Fatnassi H, Jeday M R, et al. Study of The Energetic and the Economic Feasibility of A Heating Agricultural Greenhouse Using Industrial Waste-Heat at Low Temperatures. International Journal of Energy, Environment and Economics, 2020, 25(4):269-282.

[22] Li N, Li Y M, Mu H L, et al. Convergence of China'S Agricultural Greenhouse Gases. Applied Ecology and Environmental Research, 2020, 18(1):609-624. DOI:10.15666/AEER/1801_609624

[23] Rajan, Ghimire, Sushil, et al. Tillage, crop residue, and nutrient management effects on soil organic carbon in rice-based cropping systems: A review - ScienceDirect. Journal of Integrative Agriculture, 2017, 16(1):1-15.DOI:10.1016/S2095-3119(16)61337-0

[24] Metsaranta J M, Shaw C H, Kurz W A, et al. Uncertainty of inventory-based estimates of the carbon dynamics of Canada's managed forest (1990-2014). Canadian Journal of Forest Research, 2017, 47(8):1082-1094. DOI:10.1139/CJFR-2017-0088

[25] Pryor S W, Smithers J, Lyne P, et al. Impact of agricultural practices on energy use and greenhouse gas emissions for South African sugarcane production. Journal of Cleaner Production, 2017, 141(JAN.10):137-145. DOI:10.1016/j.jclepro.2016.09.069

[26] Marucci A, Zambon I, Colantoni A, et al. A combination of agricultural and energy purposes: Evaluation of a prototype of photovoltaic greenhouse tunnel. Renewable and Sustainable Energy Reviews, 2018, 82(pt.1):1178-1186. DOI:10.1016/j.rser.2017.09.029

[27] Roopaei M, Rad P, Choo K. Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging. IEEE Cloud Computing, 2017, 4(1):10-15. DOI:10.1109/MCC.2017.5

[28] MD Smart, Cornman R S, Iwanowicz D D, et al. A Comparison of Honey Bee-Collected Pollen From Working Agricultural Lands Using Light Microscopy and ITS Metabarcoding. Environmental Entomology, 2017, 46(2):38-49. DOI:10.1093/ee/nvw159