Secured Framework for Smart Farming in Hydroponics with Intelligent and Precise Management based on IoT with Blockchain Technology

Main Article Content

Jayant Mehare
Amit Gaikwad

Abstract

Hydroponics is a type of soil-free farming that uses less water and other resources than conventional soil-based farming methods. However, due to the simultaneous supervision of multiple factors, nutrition advice, and plant diagnosis system, monitoring hydroponics farming is a difficult task. Hydroponic techniques utilizing the IoT show to deliver the finest outcomes, despite the usage of various artificial culture methods. Though, the usage of smart communication technologies and IoT exposes environments for smart farming to a wide range of cybersecurity risks and weaknesses. However, the adoption of intelligence-based controlling algorithms in the agricultural industry is a good use of current technical advancements to address these issues. This paper presented a secured framework for smart farming in hydroponics system. The proposed architecture is characterized into four-layer IoT based framework, sensor, communication, fog and cloud layer. Data analytics is performed using supervised machine learning techniques with intelligent and precise management and is applied at the fog layer for efficient computation over the cloud layer. The data security over channel is protected by using Blockchain Technology. The experimental results are evaluated and analyzed for several statistical parameters in order to improve the system efficacy.

Article Details

How to Cite
Mehare, J. . ., & Gaikwad, A. . (2023). Secured Framework for Smart Farming in Hydroponics with Intelligent and Precise Management based on IoT with Blockchain Technology. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 244–254. https://doi.org/10.17762/ijritcc.v11i9s.7418
Section
Articles

References

Modu, F., Aliyu, F., & Mabu, A. (2020). A Survey of Smart Hydroponic Systems. Advances in Science, Technology and Engineering Systems Journal, 5, 233-248.

Tambakhe, M.D., & Gulhane, V. (2020). A Survey on Techniques and Technology Used in Hydroponics System.

PrinceSamuel, S., M., K., K., S., & MangalaGowriS, G. (2020). Machine Learning and Internet of Things based Smart Agriculture. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 1101-1106.

Lakshmanan, R., Djama, M., Perumal, S., & Abdulla, R.M. (2020). Automated smart hydroponics system using internet of things. International Journal of Electrical and Computer Engineering, 10, 6389-6398.

Chowdhury, M.E., Khandakar, A., Ahmed, S., Al-Khuzaei, F., Hamdalla, J., Haque, F., Reaz, M., Shafei, A., & Al-Emadi, N. (2020). Design, Construction and Testing of IoT Based Automated Indoor Vertical Hydroponics Farming Test-Bed in Qatar. Sensors (Basel, Switzerland), 20.

Dr. V.R. Saraswathy, C. Nithiesh, S. Palani Kumaravel, S. Ruphasri, Integrating Intelligence in Hydroponic Farms, International Journal of Electrical Engineering and Technology, 11(4), 2020, pp. 150-158. http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=11&IType=4

Srivani, P., YamunaDevi, C., & ManjulaS, H. (2019). A Controlled Environment Agriculture with Hydroponics: Variants, Parameters, Methodologies and Challenges for Smart Farming. 2019 Fifteenth International Conference on Information Processing (ICINPRO), 1-8.

Kularbphettong, K., Ampant, U., & Kongrodj, N. (2019). An Automated Hydroponics System Based on Mobile Application. International Journal of Information and Education Technology, 9, 548-552.

Maldonado, A., Reyes, J.M., Breceda, H.F., Fuentes, H., Contreras, J.A., & Maldonado, U.L. (2019). Automation and Robotics Used in Hydroponic System.

Sambo, P., Nicoletto, C., Giro, A., Pii, Y., Valentinuzzi, F., Mimmo, T., Lugli, P., Orzes, G., Mazzetto, F., Astolfi, S., Terzano, R., & Cesco, S. (2019). Hydroponic Solutions for Soilless Production Systems: Issues and Opportunities in a Smart Agriculture Perspective. Frontiers in Plant Science, 10.

Kuralkar, V. P. ., Khampariya, P. ., & Bakre, S. M. . (2023). A Survey on the Investigation and Analysis for a Power System (Micro- Grid) with Stochastic Harmonic Distortion of Multiple Converters. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 72–84. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2533

Jaiswal, H., KarmaliRadha, P., Singuluri, R., & Sampson, S. (2019). IoT and Machine Learning based approach for Fully Automated Greenhouse. 2019 IEEE Bombay Section Signature Conference (IBSSC), 1-6.

Karimanzira, D., & Rauschenbach, T. (2019). Enhancing aquaponics management with IoT-based Predictive Analytics for efficient information utilization. Information Processing in Agriculture, 6, 375-385.

Kaburuan, E.R., Jayadi, R., & Harisno (2019). A Design of IoT-based Monitoring System for Intelligence Indoor Micro-Climate Horticulture Farming in Indonesia. Procedia Computer Science, 157, 459-464.

Partel, V., Kakarla, S.C., & Ampatzidis, Y. (2019). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Comput. Electron. Agric., 157, 339-350.

Georgiadis, G.P., Komninos, A., Koskeris, A., Garofalakis, J., Cuomo, F., Lorenzo, P., Gavalas, D., Hanke, S., & Mylonas, G. (2019). New publication: Improving Hydroponic Agriculture through IoT-enabled Collaborative Machine Learning.

Gertphol, S., Chulaka, P., & Changmai, T. (2018). Predictive models for Lettuce quality from Internet of Things-based hydroponic farm. 2018 22nd International Computer Science and Engineering Conference (ICSEC), 1-5.

Mehra, M., Saxena, S., Sankaranarayanan, S., Tom, R.J., & Veeramanikandan, M. (2018). IoT based hydroponics system using Deep Neural Networks. Comput. Electron. Agric., 155, 473-486.

Wongpatikaseree, K., Hnoohom, N., & Yuenyong, S. (2018). Machine Learning Methods for Assessing Freshness in Hydroponic Produce. 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), 1-4.

Alipio, M.I., Cruz, A.E., Doria, J.D., & Fruto, R.M. (2017). A smart hydroponics farming system using exact inference in Bayesian network. 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), 1-5.

Crisnapati, Padma Nyoman, I. Nyoman Kusuma Wardana, I. Komang Agus Ady Aryanto, and Agus Hermawan. "Hommons: Hydroponic management and monitoring system for an IOT based NFT farm using web technology." In Cyber and IT Service Management (CITSM), 2017 5th International Conference on, pp. 1-6. IEEE, 2017.

Chen, Qi, Xinlei Wang, and Fan Liang. "Intelligent Control and Information Management System for Plant Growth Cabinet Based on Internet of Things." In Proceedings of the 2017 International Conference on Management Engineering, Software Engineering and Service Sciences, pp. 246-249. ACM, 2017.

Yi-Bing Lin. "Intelligent Plant Care Hydroponic Box Using IoTtalk."In Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2016 IEEE International Conference on, pp. 398-401. IEEE, 2016.

Saaid, M. F., N. A. M. Yahya, M. Z. H. Noor, and MSA Megat Ali. "A development of an automatic microcontroller system for Deep Water Culture (DWC)." In Signal Processing and its Applications (CSPA), 2013 IEEE 9th International Colloquium on, pp. 328-332. IEEE, 2013.

F. D. Ribeiro, F. Caliva, M. Swainson, K. Gudmundsson, G. Leontidis, and S. Kollias, “An adaptable deep learning system for optical character verification in retail food packaging,” in 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1–8, Rhodes, Greece, 2018.

K. Salah, N. Nizamuddin, R. Jayaraman, and M. Omar, “Blockchain-based soybean traceability in agricultural supply chain,” IEEE Access, vol. 7, pp. 73295 73305, 2019.

Z. Shahbazi and Y.-C. Byun, “A procedure for tracing supply chains for perishable food based on blockchain, machine learning and fuzzy logic,” Electronics, vol. 10, no. 1, 2021.

Yuan, Y.; Jiang, X.; Liu, X. Predictive maintenance of shield tunnels. Tunn. Undergr. Space Technol. 2013, 38, 69–86. [CrossRef]

S. Cho and S. Lee, "Survey on the Application of BlockChain to IoT," 2019 International Conference on Electronics, Information, and Communication (ICEIC), Auckland, New Zealand, 2019, pp. 1-2, doi: 10.23919/ELINFOCOM.2019.8706369.

Ms. Pooja Sahu. (2015). Automatic Speech Recognition in Mobile Customer Care Service. International Journal of New Practices in Management and Engineering, 4(01), 07 - 11. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/34

Dorri, Ali, Kanhere, Salil, Jurdak, Raja, &Gauravaram, Praveen (2019) LSB: A lightweight scalable blockchain for IoT security and anonymity. Journal of Parallel and Distributed Computing, 134, pp. 180-197.

S. Tanwar, Q. Bhatia, P. Patel, A. Kumari, P. K. Singh and W. Hong, "Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward," in IEEE Access, vol. 8, pp. 474-488, 2020, doi: 10.1109/ACCESS.2019.2961372.

R. Mahlous and A. Ara, "The Adoption of Blockchain Technology in IoT: An Insight View," 2020 6th Conference on Data Science and Machine Learning Applications (CDMA), Riyadh, Saudi Arabia, 2020, pp. 100-105, doi: 10.1109/CDMA47397.2020.00023.

Francesco Restuccia, Salvatore D'OroandSalil S. Kanhere, TommasoMelodia, Sajal K. Das,”Blockchain for the Internet of Things: Present and Future”, 2019, https://arxiv.org/abs/1903.07448v1

M. Singh, A. Singh and S. Kim, "Blockchain: A game changer for securing IoT data," 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, 2018, pp. 51-55, doi: 10.1109/WF-IoT.2018.8355182.

Damianou, C. M. Angelopoulos and V. Katos, "An Architecture for Blockchain over Edge-enabled IoT for Smart Circular Cities," 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece, 2019, pp. 465-472, doi: 10.1109/DCOSS.2019.00092.

Thota, D. S. ., Sangeetha, D. M., & Raj , R. . (2022). Breast Cancer Detection by Feature Extraction and Classification Using Deep Learning Architectures. Research Journal of Computer Systems and Engineering, 3(1), 90–94. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/48

Q. Ren, K. L. Man, M. Li and B. Gao, "Using Blockchain to Enhance and Optimize IoT-based Intelligent Traffic System," 2019 International Conference on Platform Technology and Service (PlatCon), Jeju, Korea (South), 2019, pp. 1-4, doi: 10.1109/PlatCon.2019.8669412.

Kamilaris, Andreas & Fonts, Agusti&PrenafetaBoldú, Francesc. (2019). The Rise of Blockchain Technology in Agriculture and Food Supply Chains. Trends in Food Science & Technology. 10.1016/j.tifs.2019.07.034.

S. Umamaheswari, S. Sreeram, N. Kritika and D. R. JyothiPrasanth, "BIoT: Blockchain based IoT for Agriculture," 2019 11th International Conference on Advanced Computing (ICoAC), Chennai, India, 2019, pp. 324-327, doi: 10.1109/ICoAC48765.2019.246860.