IoT-based Spatial Monitoring and Environment Prediction System for Smart Greenhouses

Authors

Keywords:

Climate prediction, Internet of things, LPWAN, Machine learning, Precision agriculture, Smart greenhouse

Abstract

Agriculture has seen several technological transformations in recent years, allowing accelerated growth in production to meet the greater consumer demand for food. In particular, the adoption of the internet of things (IoT) is rapidly transforming the future of agriculture. In this paper, we present a data-driven climate prediction model for greenhouses using an effective and low-cost IoT-based spatial monitoring system. The IoT infrastructure comprises four stages: data gathering, transmission, analysis and processing of information, and visualization. This system was deployed and tested in a real greenhouse for three months, monitoring the temperature, relative humidity, and CO2 levels. The behavior of these environmental variables were predicted 24h in advance, obtaining an advantage in prediction accuracy. Additionally, we developed a spatial monitoring strategy based on packing density theory as a solution to the climate variability within greenhouses, offering a compromise between effectiveness and cost.

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Author Biographies

Carlos Alberto Hernández-Morales, Facultad de ciencias, Universidad Autónoma de San Luis Potosí, México

Carlos A. Hernández-Morales received the Bachelor´s degree in telematics engineering from the Polytechnic University of San Luis Potosi, Mexico in 2016 and the master´s degree in electronic engineering from the Autonomous University of San Luis Potosi, Mexico in 2019. He is currently a Ph.D. student at the Autonomous University of San Luis Potosi. His research interests include Internet of things, embedded systems, machine learning, smart agriculture, wireless sensor networks and analytics.

Jose Martín Luna-Rivera, Facultad de ciencias, Universidad Autónoma de San Luis Potosí, México

J.M. Luna-Rivera received the B.S. and M.Eng in electronics engineering from Autonomous University of San Luis Potosi, Mexico, in 1997 and 1998, respectively. He received the Ph.D. degree in electrical engineering from the University of Edinburgh, UK, in 2003. He is currently a full-time professor at the Science faculty of the Autonomous University of San Luis Potosi, Mexico. In 2014, he received the "University Award for Technological and Scientific Research" as a Young Researcher from the Autonomous University of San Luis Potosi. His research interests focus on applying signal processing for wireless communication systems, including developing techniques and algorithms for array signal detection, efficient modulation schemes, transmit/receive diversity schemes, channel modeling, signal precoding, interference cancellation, and power control techniques. Applications of this research work include Visible Light Communications, Vehicular Communications, Internet of Things, and Mobile Communications, which has resulted in over 100 journals and refereed conference publications.

Federico Villarreal-Guerrero, Universidad Autónoma de Chihuahua, México

Professor and Researcher at the School of Animal Sciences and Ecology, University of Chihuahua, Mexico. B.S. Agricultural Engineer, graduated in 2011, University of Chapingo, Mexico; M.Sc. Horticultural Sciences, graduated in 2005, University of Chihuahua, Mexico; Ph.D. Agricultural and Biosystems Engineering, graduated in 2011, University of Arizona, USA. Worked as a Professor and Researcher at the University of San Luis Potosi during 2011-2014 and at the University of Chihuahua from 2014 to date. Current and previous research interests focus on physiological plant responses to the environment under both, natural and controlled conditions. That includes responses to abiotic stress and climate change.

Pablo Delgado-Sanchez, Facultad de Agronomía y Veterinaria, Universidad Autónoma de San Luis Potosí, México

Professor Researcher, Doctor in Applied Sciences, 2011, Instituto Potosino de Investigacion Cientifica y Tecnologica IPICYT, A.C. Agricultural Chemist, 2004, Universidad Veracruzana. Professor Researcher and Coordinator of the Doctorado en Ciencias Agropecuarias, Facultad de Agronomia y Veterinaria, Universidad Autónoma de San Luis Potosí. My research is focused on the study of physiological and molecular mechanisms of plants exposed to abiotic stress conditions (drought, salinity, and solar radiation). Member of the Sociedad Mexicana de Bioquimica, A.C., Sociedad Mexicana de Biotecnología y Bioingenieria, and the Sistema Nacional de Investigadores Nivel I. AgroBIO-México Award 2012 for Best D.Sc. thesis of Research in Plant Biotechnology.

Zoe Arturo Guadiana-Alvarado, Facultad de ingeniería, Universidad Autónoma de San Luis Potosí, México.

He received the B.S. degree in agricultural engineering, the master´s degree in technology and water management, and the Ph.D. degree in environmental science from the Autonomous University of San Luis Potosi, Mexico in 2007, 2014, and 2020 respectively. He is currently a research professor at engineering faculty of the Autonomous University of San Luis Potosi, Mexico since 2013. His research interests include protected agriculture, environmental sciences and sustainable development.

References

N. Genhua and J. Masabni. “Plant Production in Controlled Environments,” Horticulturae, vol.4, no. 4:28, 2018. https://doi.org/10.3390/horticulturae4040028

X. Shi et al., “State-of-the-Art Internet of Things in Protected Agriculture,” Sensors, vol. 19, no. 8, p. 1833, Apr. 2019, doi:10.3390/s19081833

S. M.-e. Rezvani, H. Z. Abyaneh, R. R. Shamshiri, S. K. Balasundram, V. Dworak, M. Goodarzi, M. Sultan and B. Mahns. “IoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato” Sensors 20, no. 22:6474. 2020. https://doi.org/10.3390/s20226474

R. K. Kodali, S. C. Rajanarayanan and L. Boppana, “IoT based Weather Monitoring and Notification System for Greenhouses,” in 11th International Conference on Advanced Computing (ICoAC), 2019. doi:10.1109/icoac48765.2019.24686

M. C. Sánchez-Guerrero, P. Lorenzo, E. Medrano, N. Castilla, T. Soriano, A. Baille, “Effect of variable CO2 enrichment on greenhouse production in mild winter climates”, Agricultural and ForestMeteorology, (132), pp. 244-252, 2005.

S. Santiteerakul, A. Sopadang, K. Y. Tippayawong, and K. Tamvimol, “The Role of Smart Technology in Sustainable Agriculture: A Case Study of Wangree Plant Factory”, Sustainability, vol. 12, no. 11: 4640, 2020. https://doi.org/10.3390/su12114640.

M. Ayaz, M. Ammad-Uddin, Z. Sharif, A.Mansour and E.M. Aggoune, “Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk”, in IEEE Access, vol. 7, pp. 129551-129583, 2019.

Y. Liu, X. Ma, L. Shu, G. P. Hancke and A. M. Abu-Mahfouz, “From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges´´, in IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 4322-4334, June 2021, doi: 10.1109/TII.2020.3003910.

M. S. Farooq, S. Riaz, A. Abid, K. Abid and M. A. Naeem, “A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming”, in IEEE Access, vol. 7, pp. 156237- 156271, 2019, doi:10.1109/ACCESS.2019.2949703.

N.N. Misra, Y. Dixit, A. Al-Mallahi, M.S. Bhullar, R. Upadhyay, A. Martynenko, “IoT, Big data and artificial intelligence in agriculture and food industry,” IEEE Internet Things J. 2020. http://dx.doi.org/10.1109/JIOT.2020.2998584.

E. A. Abioye, O. Hensel, T. J. Esau, Olakunle Elijah, Mohamad S.Z. Abidin, A. S. Ayobami, O. Yerima, and A. Nasirahmadi. 2022. “Precision Irrigation Management Using Machine Learning and Digital Farming Solutions” AgriEngineering, vol. 4, no. 1: 70-103. https://doi.org/10.3390/agriengineering4010006

T. Pisanu, S. Garau, P. Ortu, L. Schirru, and C. Macciò, “Prototype of a Low-Cost Electronic Platform for Real Time Greenhouse Environment Monitoring: An Agriculture 4.0 Perspective,” Electronics vol. 9, no. 5:726, 2020. https://doi.org/10.3390/electronics9050726

G. E. Vazquez-Becerra, “In-Field Electronic Based System and Methodology for Precision Agriculture and Yield Prediction in Seasonal Maize Field”, IEEE LAT AM T, vol. 17, no. 10, pp. 1598–1606, Dec. 2019.

A. F. Subahi and K. E. Bouazza, “An Intelligent IoT-Based System Design for Controlling and Monitoring Greenhouse Temperature,” in IEEE Access, vol. 8, pp. 125488-125500, 2020, doi: 10.1109/ACCESS.2020.3007955.

P. K. Tripathy, A. K. Tripathy, A. Agarwal, and S. P. Mohanty, “MyGreen: An IoTEnabled Smart Greenhouse for Sustainable Agriculture,” IEEE Consumer Electronics Magazine, 10(4), 57–62, 2021. doi:10.1109/mce.2021.3055930

G.W. Archbold, “pH Measurement IoT System for Precision Agriculture Applications”, IEEE LAT AM T, vol. 17, no. 5, pp. 823–832, Nov. 2019.

I. Ullah, M. Fayaz, N. Naveed and D. Kim, “ANN Based Learning to Kalman Filter Algorithmfor Indoor Environment Prediction in Smart Greenhouse,” in IEEE Access, vol. 8, pp. 159371-159388, 2020, doi:10.1109/ACCESS.2020.3016277.

G. Codeluppi, A. Cilfone, L. Davoli, and G. Ferrari, “AI at the Edge: A Smart Gateway for Greenhouse Air Temperature Forecasting,” 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 2020. doi:10.1109/metroagrifor50201.202

G. Storey, M. Qinggang, and L. Baihua, “Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture,” Sustainability vol. 14, no. 3: 1458, 2022 https://doi.org/10.3390/su14031458

L. Gong, M. Yu, S. Jiang, V. Cutsuridis, and S. Pearson, “Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN,” Sensors vol. 21, no. 13: 4537, 2021. https://doi.org/10.3390/s21134537

T. Konstantinos, A. Al-Zoubi, N. Christofides, C. Zannettis, M. Chrysostomou, S. Panteli, and A. Antoniou. “Reliable IoT-Based Monitoring and Control of Hydroponic Systems,” Technologies vol. 10, no. 1: 26, 2022. https://doi.org/10.3390/technologies10010026

J. Yang, M. Liu, J. Lu, Y. Miao, M. A. Hossain, and M. F. Alhamid, “Botanical Internet of Things: Toward Smart Indoor Farming by Connecting People, Plant, Data and Clouds,” Mobile Networks and Applications, 23(2), 188–202, 2017. doi:10.1007/s11036-017-0930-x

M. C. Al Fajar and O. N. Samijayani, “Real time Greenhouse Environment Monitoring Based on LoRaWAn Protocol using Grafana,” 2021 International Symposium on Electronics and Smart Devices (ISESD), 2021, pp. 1-5, doi: 10.1109/ISESD53023.2021.9501628.

R. K. Singh, M. Aernouts, M. De Meyer, M. Weyn, and R. Berkvens. “Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses,” Sensors vol. 20, no. 7: 1827, 2020. https://doi.org/10.3390/s20071827

G. Codeluppi, L. Davoli, and G. Ferrari. “Forecasting Air Temperature on Edge Devices with Embedded AI,” Sensors 21, no. 12: 3973, 2021. https://doi.org/10.3390/s21123973

A. Ali, and H. S. Hassanein.“Time-Series Prediction for Sensing in Smart Greenhouses,” GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020. doi:10.1109/globecom42002.2020.93

X. Chen, X. Wang, and H. Shen, “Design of Greenhouse Environment Monitoring System based on NB-IoT And Edge Computing,” 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2021. doi:10.1109/iaeac50856.2021.93906

A. Mellit, M. Benghanem, O. Herrak, A. Messalaoui, “Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks,” Energies 2021, 14, 5045. https://doi.org/10.3390/en14165045

A. Goap, D. Sharma, A.K. Shukla, C. Rama Krishna, “An IoT based smart irrigation management system using Machine learning and open-source technologies,” Computers and Electronics in Agriculture, Volume 155, 2018, Pages 41-49, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2018.09.040.

Ritesh K. Singh, H. Rahmani Mohammad, M. Weyn, and R. Berkvens. “Joint Communication and Sensing: A Proof of Concept and Datasets for Greenhouse Monitoring Using LoRaWAN,” Sensors 22, no. 4: 1326, 2022 https://doi.org/10.3390/s22041326

Konstantinos P. Ferentinos, N. Katsoulas, A. Tzounis, T. Bartzanas, C. Kittas, “Wireless sensor networks for greenhouse climate and plant condition assessment,” Biosystems Engineering, Vol. 153, 2017, Pages 70-81, ISSN 1537-5110, https://doi.org/10.1016/j.biosystemseng.2016.11.005.

B. Erfianto, A. Rakhmatsyah, and E. Ariyanto, (2020). “Micro-Climate Control for Hydroponics in Greenhouses,” 2020 8th International Conference on Information and Communication Technology (ICoICT). doi:10.1109/icoict49345.2020.9166

H. Liu, Z. Meng and S. Cui, “A Wireless Sensor Network Prototype for Environmental Monitoring in Greenhouses,” 2007 International Conference on Wireless Communications, Networking andMobile Computing, Shanghai, 2007, pp. 2344-2347, doi:10.1109/WICOM.2007.584.

S. Lee, I. Lee, U. Yeo, R. Kim, and J. Kim, “Optimal sensor placement for monitoring and controlling greenhouse internal environments,” Biosystems Engineering, 188, 190–206, 2019. doi:10.1016/j.biosystemseng.2019.

D. D. Uyeh et al., “A Reinforcement Learning Approach for Optimal Placement of Sensors in Protected Cultivation Systems,” in IEEE Access, vol. 9, pp. 100781-100800, 2021, doi:10.1109/ACCESS.2021.3096828.

B. Mazon-Olivo and A. Pan, “Internet of Things: State-of-the-art, Computing Paradigms and Reference Architectures”, IEEE LAT AM T, vol. 20, no. 1, pp. 49–63, May 2021.

R.K. Singh, R. Berkvens and M. Weyn, “Agrifusion: An architecture for IoT and emerging technologies based on a precision agriculture survey,” IEEE Access 9 (2021) 136253–136283, http://dx.doi.org/10.1109/ACCESS.2021.3116814.

C. A. Hernández-Morales, J.M. Luna-Rivera, Rafael Perez-Jimenez, “Design and deployment of a practical IoT-based monitoring system for protected cultivations,” Computer Communications, Volume 186, 2022, Pages 51-64, ISSN 0140-3664, doi.org/10.1016/j.comcom.2022.01.009.

www.analog.com. ’RTD Interfacing and Linearization Using an ADuC8xxMicroConverter’, 2014. Available online: https://www.analog.com/media/en/technical-documentation/applicationnotes/AN709_0.pdf. [Accessed: 20-Feb-2022].

G. Coulby, Adrian K. Clear, O. Jones, A. Godfrey, “Low-cost multimodal environmental monitoring based on the Internet of Things, Building and Environment”, Volume 203, 2021, 108014, ISSN 0360- 1323, https://doi.org/10.1016/j.buildenv.2021.108014.

P. G. Szabo, M. C. Markot, T. Csendes, E. Specht, L. G. Casado, and I. Garcia, “New approaches to circle packing in a square: with program codes,” in Optimization and Its Applications, vol. 6, Springer, New York, NY, USA, 2007.

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Published

2023-03-23

How to Cite

Hernández-Morales, C. A., Luna-Rivera, J. M., Villarreal-Guerrero, F., Delgado-Sanchez, P., & Guadiana-Alvarado, Z. A. (2023). IoT-based Spatial Monitoring and Environment Prediction System for Smart Greenhouses. IEEE Latin America Transactions, 21(4), 602–611. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/7524

Issue

Vol. 21 No. 4 (2023): Ordinary Issue

Section

Electronics