Authors : Ukoba J. O.; Anuku E. O.

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/muk25uxh

Scribd : https://tinyurl.com/5n93j96z

DOI : https://doi.org/10.38124/ijisrt/25feb1515

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Hydroponics is a soilless farming technique in which the plants are irrigated with a nutrient solution consisting of water and compounds necessary to provide all the essential elements for normal mineral nutrition. Increase in population, industrialization which has lead to pollution and change in climatic condition has pose a serious threat to food security. This paper therefore explores the integration of Deep Learning (DL) and Business Intelligence (BI) in smart hydroponic greenhouse systems, aiming to optimize cultivation through data-driven automation. A conceptual architecture is presented, highlighting the flow of information from sensor inputs and cameras, through a Raspberry Pi and IoT gateway, to a central database. ANNs, including classification and prediction models, process this data, enabling automated control of actuators and providing actionable insights through a BI dashboard. The discussion of findings, based on reviewed literature and the proposed architecture, reveals a strong trend towards leveraging advanced technologies for improved efficiency, accuracy, and productivity in hydroponic agriculture. The integration of deep learning for tasks like disease detection and yield prediction, coupled with BI for data visualization and decision support, underscores the potential of these technologies to revolutionize hydroponic practices. This research emphasizes the importance of data-driven approaches, IoT infrastructure, and closed-loop control systems in creating intelligent and sustainable greenhouse environments.

Keywords : Hydroponics, Greenhouses, Deep Learning, Artificial Neural Network, Business Intelligence, Smart System.

References :

1. Abdel-Basset, M., Hawash, H., & Abdel-Fatah, L. (2024). Artificial Intelligence and Internet of Things in Smart Farming. [HTML]
2. Alipio M.,  Dela Cruz A. E. M., Doria J. D.A. and Fruto R. M. S. (2017). A smart hydroponics farming system using exact inference in bayesian network, Proceedings of IEEE 6th Global Conference on Consumer Electronics (GCCE 2017); Pp. 1-5 DOI: 10.1109/GCCE.2017.8229470
3. Arias, R., Lee, T. C., Specca, D., Janes, H. (2000). Quality comparison of hydroponic tomatoes (Lycopersicon esculentum) ripened on and offvine. Journal of Food Science, 65(3), 545–548.
4. Asy'ari, H., Hidayat, N., & Prasetyo, Y. (2023). Forecasting Plant Growth in Hydroponic Farm Using ARIMA Time Series Approach. 2023 6th International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE), 1-6. .
5. Ayala-Silva, T., & Beyl, C. A. (2002). Classification of nutrient deficiency in wheat using hyperspectral data and multilayer perceptron neural networks. Transactions of the ASAE, 45(5), 1603-1610.
6. Baras T. (2018). DIY Hydroponic Gardens: How to Design and Build an Inexpensive System for Growing Plants in Water. Cool Springs Press. Retrieved on July 25th, 2023 from https://books.google.com.sa/books?id=rwlMDwAAQBAJ
7. Bardi & Palazzi  & Palazzi  S. & Palazzi C.E. (2022). Smart hydroponic greenhouse: Internet of Things and soilless farming. In Conference on Information Technology for Social Good (GoodIT'22),Limassol, Cyprus. ACM, New York, USA, 6, https://doi.org/10.1145/3524458.3547221
8. Bridgewood, L. (2003). Hydroponics: Soilless gardening explained. Ramsbury, Marlborough, Wiltshire: The Crowood Press Limited.
9. Buchanan, D. N., Omaye, S. T. (2013). Comparative Study of Ascorbic Acid and Tocopherol Concentrations in Hydroponic- and Soil-Grown Lettuces. Food and Nutrition Sciences, 04(10), 1047–1053.
10. Bulut, Y., & Hacıbeyoğlu, M. (2023). Machine Learning and Deep Learning Algorithms for Smart Hydroponic Farm. International Journal of Intelligent Systems and Applications in Engineering, 11(13s), 35-42.
11. Bussa, S. (2023). Enhancing BI Tools for Improved Data Visualization and Insights. academia.edu
12. Despommier, D. (2009). The rise of vertical farms. Scientific American, vol. 301, no. 5, pp. 80-87.
13. Gartphol, S., Choochart, P., & Choochart, C. (2018). Predictive models for lettuce quality from Internet of Things-based hydroponic farms. 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 781-784.
14. Gupta G. (2004). Text Book of Plant Diseases. Discovery Publishing House. Retrieved on July 25th, 2023 from https://books.google.com.sa/books?id=OuoicDXQ-xYC
15. Ifechukwude l.A, Okhionkpamwunyi P.E., and Uwana I. U. (2022). Trends in greenhouse farming technology: A review; Journal of Food, Agriculture & Environment, Vol.19 (2): 6 9 - 7 4
16. Ikegwu, A. C., Nweke, H. F., Anikwe, C. V., Alo, U. R., & Okonkwo, O. R. (2022). Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions. Cluster Computing, 25(5), 3343-3387. academia.edu
17. JSM L. M. and Sridevi  C. (2014). Design of efficient hydroponic nutrient solution control system using soft computing based solution grading, International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC). IEEE, pp. 148–154.
18. Kaewwiset T. and Yooyativong T (2017). Estimation of electrical conductivity and ph in hydroponic nutrient mixing system using linear regression algorithm, International Conference on Digital Arts, Media and Technology (ICDAMT). IEEE, pp. 1–5.
19. Ke W. and Xiong Z.(2008), “Di_erence of growth, copper accumulation and mineral element uptake in two elsholtzia haichowensis populations under copper and mineral nutrition stress,” 2nd International Conference on Bioinformatics and Biomedical Engineering. IEEE, 4704–4708.
20. Khan F. A,  Kurklu A., Ghafoor A., Ali Q., Umair M., Shahzaib (2018). A review on hydroponic greenhouse cultivation for sustainable agriculture, International Journal of Agriculture, Environment and Food Sciences, 2(2):59-66.
21. Koyama, M., Nakamura, C., Kozo, N. (2013). Changes in phenols contents from buckwheat sprouts during growth stage. Journal of Food Science and Technology, 50(1), 86–91.
22. Li D. and Dong Y. (2014). Deep learning: methods and applications. Foundations and Trends R in Signal Processing, 7(3{4):197{387, 2014. doi: 10.1007/ 978-981-13-3459-73.
23. Mashumah, A., Nugroho, A. S., & Heryadi, Y. (2018). Fuzzy logic control for electrical conductivity (EC) level regulation in nutrient film technique (NFT) hydroponics. 2018 International Conference on Information Technology (ICIT), 137-142.
24. Modu F., Adam A.,  Aliyu2 F., Mabu A., Musa M. (2020). A survey of smart hydroponic systems. Advances in Science, Technology and Engineering Systems Journal, Vol. 5, No. 1, 233-248
25. Muro J., Irigoyen I., Samitier P., Mazuela P., Salas M., Soler J., and Urrestarazu M. (2004).Wood fiber as growing medium in hydroponic crop, International Symposium on Soilless Culture and Hydroponics, 697,pp. 179–185.
26. Omol, E., Mburu, L., & Abuonji, P. (2024). Unlocking digital transformation: The pivotal role of data analytics and business intelligence strategies. International Journal of Knowledge Content Development & Technology, 14(3), 77-91. koreascience.kr
27. Pancić, D., Popović-Pantić, S., & Pantić, M. (2023). The influence of business intelligence on firm performance through the mediating roles of the adoption of big data analytics and blockchain. Sustainability, 15(18), 13615.
28. Panwar N., Kaushik S. and Kothari S. (2011). Solar greenhouse an option for renewable and sustainable farming, Renewable Sustain. Energy Rev.; 15(8), pp. 3934–3945.
29. Rajkumar G.R. and Chachadi A.D. (2021). Development of automated hydroponic system for smart agriculture; International Research Journal of Engineering and Technology (IRJET); Vol 8(6), pp. 1273 – 1278
30. Rajkunwar C., Vaibhav B., Swagat W. (2024). Smart hydroponics farming system using AI; International Research Journal of Modernization in Engineering Technology and Science, Vol. 6(5) pp. 1548 – 1552
31. Raju R. S. V. S. Bhasker D., Ravi K.V. P., Murali Y., Marlene G.V.D. and Manoj K.M. (2022). Design and implementation of smart hydroponics farming using IoT-based AI controller with mobile application system,  Journal of Nanomaterials, Article ID 4435591, 12, https://doi.org/10.1155/2022/4435591
32. Raviv M.  and Lieth J. (2007). Soilless Culture: Theory and Practice. Elsevier Science, 2007. Retrieved on July 25th, 2023 from https://books.google.com.sa/books?id=NvDHJxRwsgYC
33. Resh, H.M. (1993). Hydroponic food production. California: Woodbridge Press Publishing Company.
34. RIRDC. (2001). Hydroponics as an Agricultural Production System, Publication No 01/141, Project No HAS-9A, Rural Industries Research and Development Corporation (RIRDC), Australian Government, Kingston, PO Box 4776, ACT 2604.
35. Singh D., Davidson J., and Books M. (2016), Introduction to Hydroponics - Growing Your Plants Without Any Soil, ser. Gardening Series. Mendon Cottage Books. Retrieved on July 25th, 2023 from https://books.google.com.sa/books?id=RAMtDQAAQBAJ
36. Solanki, A., Jain, K., & Jadiga, S. (2024). Building a Data-Driven Culture: Empowering Organizations with Business Intelligence. International Journal of Computer Trends and Technology, 72(2), 46-55. researchgate.net
37. Son J.E., Kim H.J., Ahn T.I. (2020). Hydroponic systems. In: Kozai T, Niu G, Takagaki M (eds) Plant factory: an indoor vertical farming system for efficient quality food production, 2nd edn. Academic Press, Cambridge, pp 273–283.
38. Suzui N., Kawachi N., Yamaguchi M., Ishioka N. S., Fujimaki S. (2009). “A monitoring system of radioactive tracers in hydroponic solution for research on plant physiology,”  1st International Conference on Advancements in Nuclear Instrumentation, Measurement Methods and their Applications. IEEE, pp. 1–3.
39. Syed, S., & Nampally, R. C. R. (2021). Empowering Users: The Role Of AI In Enhancing Self-Service BI For Data-Driven Decision Making. Educational Administration: Theory and Practice. Green Publication. https://doi. org/10.53555/kuey. v27i4, 8105. researchgate.net
40. Tambakhe, M. D.and Gulhane, V. S. (2022). An intelligent crop growth monitoring system using IoT and machine learning; International Journal of Health Sciences, 6(S8), 230–241, https://doi.org/10.53730/ijhs.v6nS8.9686
41. Udeh, C. A., Orieno, O. H., Daraojimba, O. D., Ndubuisi, N. L., & Oriekhoe, O. I. (2024). Big data analytics: a review of its transformative role in modern business intelligence. Computer Science & IT Research Journal, 5(1), 219-236. fepbl.com
42. United Nations (2016). World Population Prospects (Key Finding and Advance Tables). Retrieved on 25/04/2023 from  https://esa.un.org/unpd/wpp/publications/files/key_findings_wpp_2015.pdf
43. Wang H., Wang Y., Yang Y. (2011). “E_ects of exogenous phenolic acids onroots of poplar hydroponic cuttings,” International Conference on Remote Sensing, Environment and Transportation Engineering. IEEE,  pp. 8245–8249.
44. Wongpatikaseree, C., Phatthanasiri, T., & Jantrakulchai, N. (2018). Freshness detection of vegetation harvested from a smart hydroponic system using image processing and machine learning. 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 785-788.