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Stream/Area:
-
Subject:
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Paper ID:
10.55041/IJSREM.NCET020
Volume:
10 |
10 |
Oct 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Artificial intelligence & computer science
Dr MGR Arts and Science College for Women
Artificial intelligence & computer science
Dr MGR Arts and Science College for Women
Artificial intelligence & computer science
Dr MGR Arts and Science College for Women
Artificial intelligence & computer science
Dr MGR Arts and Science College for Women
Rapid city expansion has increased the demand for smart and sustainable gardening solutions. This paper proposes an IoT-based smart terrace garden integrated with a smart homesystem that helps simplify terrace garden management. The system continuously monitors soil moisture, temperature, and plant health, detects pest and insect attacks on individual plants, and sends real-time alerts to the user’s Smartphone. In addition, it recommends suitable treatments to protect plant health. The system monitors the growth of each plant and predicts important stages such as flowering, fruiting, and harvesting. The proposed system helps users manage their gardens more efficiently while protecting crops from birds, crows, and squirrels. The system demonstrates the potential of IoT-driven automation in promoting sustainable urban gardening and enhancing the quality of life for elderly users.
KEYWORDS
In this study, an IoT-enabled smart terrace garden along with a smart home system was described for sustainable urban agriculture. In the architecture proposed here, environmental sensing, micro-irrigation control, pest and disease identification along with remedies, plant growth stage prediction, and a harmless bird- and animal-deterring system were all integrated into one smartphone app along with the existing smart home system in the house. Prototyping results carried out on the testbed terrace garden showed that there is proper environmental sensing, pest/growth alerts, bird/squirrel deterring system, and reduced manual monitoring burden for users, especially the elderly population. Future research will be aimed at collecting more data for pest and disease classification in order to have more accurate early identification, introducing solar-powered and energy-neutral sensor nodes, and conducting a larger field test across many houses for further validation of the system.
[1] S. R. Laha, B. K. Pattanayak, S. Pattnaik, D. Mishra, D. S. K. Nayak, and B. B. Dash, "An IoT-based soil moisture management system for precision agriculture: Real-time monitoring and automated irrigation control," in Proc. IEEE Conf., 2023. [2] P. Rerkrai and V. Areerak, "IoT based soil moisture sensor for smart farming," in Proc. IEEE Conf., 2021. [3] S. K. Bhoi et al., "IoT-driven soil moisture monitoring for optimizing irrigation," in Proc. IEEE Conf., 2024. [4] R. Widodo, A. Setiawan, and B. Prasetyo, "Smart home garden monitoring system based on Internet of Things," in Proc. IEEE Conf., 2024. [5] M. H. Jumat, M. S. Nazmudeen, and A. T. Wan, "Smart farm prototype for plant disease detection, diagnosis and treatment using IoT device in a greenhouse," in Proc. Int. Conf. Robotics, Automation and Sciences, 2018, pp. 48-49. [6] P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, "Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks," IEEE Access, vol. 7, pp. 59069-59080, 2019. [7] S. Ahmed, M. B. Hasan, T. Ahmed, M. R. K. Sony, and M. H. Kabir, "Less is more: Lighter and faster deep neural architecture for tomato leaf disease classification," IEEE Access, vol. 10, pp. 68868-68884, 2022. [8] Z. Liu, R. N. Bashir, S. Iqbal, M. M. A. Shahid, M. Tausif, and Q. Umer, "Internet of Things (IoT) and machine learning model of plant disease prediction: Blister blight for tea plant," IEEE Access, vol. 10, pp. 44934-44944, 2022. [9] M. M. U. Saleheen, M. S. Islam, R. Fahad, M. J. B. Belal, and R. Khan, "IoT-based smart agriculture monitoring system," in Proc. IEEE Int. Conf. Artificial Intelligence in Engineering and Technology (IICAIET), 2022, pp. 1-6. [10] J. Boulent, S. Foucher, J. Theau, and P.-L. St-Charles, "Convolutional neural networks for the automatic identification of plant diseases," Frontiers in Plant Science, vol. 10, 2019.
Publication Date
10/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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