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Smart city article details
| Title | Iot Based Smart Plant Growing And Caring Platform For Urban Environments |
|---|---|
| ID_Doc | 33717 |
| Authors | Ilangaweera I.A.S.; Naramada S.E.M.H.; Sajith N.T.M.; Aashiq M.N.M.; Hinas M.N.A. |
| Year | 2024 |
| Published | Proceedings of ICITR 2024 - 9th International Conference on Information Technology Research: Harnessing the Potential of Information Technology |
| DOI | http://dx.doi.org/10.1109/ICITR64794.2024.10857784 |
| Abstract | This paper explores the design, implementation, and performance evaluation of an innovative IoT enabled automatic plant growing and caring platform tailored for urban environments. The proposed system utilizes supervised and unsupervised machine learning algorithms to make decisions toward enhancing the growth and health of the plants on the platform with minimum human intervention. Increasing urbanization and busy lifestyles have led to increased interest in smart gardening solutions that optimize plant care and resource utilization. This research delves into the integration of IoT technologies and machine learning algorithms to address challenges faced with urban gardening and contribute to the ever-expanding body of literature on IoT applications for smart cities. The proposed platform comprises multiple sensors placed to collect vital parameters such as soil moisture, ambient temperature, and humidity. A simple camera collects images of the plants daily. The collected data is transmitted via Wi-Fi to an online database for storage and analysis. A machine learning algorithm analyses the collected environmental data to make informed decisions such as predicting the optimal watering schedule. A pump integrated into the platform is activated based on the schedule generated by the algorithm. The schedule prediction is based on plant species and real-time environmental data. Also, an unsupervised algorithm is used to determine the health status of the plants. The images taken daily are analyzed by the unsupervised algorithm to detect simple abnormalities in the plants to alert the owners to attend to the issues. The system was able to monitor the vital parameters and care for the plants independently. © 2024 IEEE. |
| Author Keywords | Automation; IOT; Machine Learning; Raspberry-pi; Smart Farming |
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