Ali, Mohammed A. H. and Moiduddin, Khaja and Nukman, Yusoff and Abd Razak, Bushroa and Aboudaif, Mohamed K. and Thangaraj, Muthuramalingam (2024) An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating. PeerJ Computer Science, 10. e2448. ISSN 2376-5992, DOI https://doi.org/10.7717/peerj-cs.2448.
Full text not available from this repository.Abstract
This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AIpowered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO2) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO2 concentration level, and video processing-based pests' detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.
| Item Type: | Article |
|---|---|
| Funders: | King Saud Universiy, Riyadh, Saudi Arabia (RSPD2024R1076) |
| Uncontrolled Keywords: | Automated AI-Powered IoT farming; Enhanced Laser Simulator Logic (ELSL); IoT monitoring; Image and signal processing; CO2 detector and soil moisture sensors; Chili |
| Subjects: | T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Faculty of Engineering Faculty of Engineering > Department of Mechanical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 21 Feb 2025 02:58 |
| Last Modified: | 21 Feb 2025 02:58 |
| URI: | http://eprints.um.edu.my/id/eprint/47335 |
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