IOT- enabled hydroponic farming: a solution for high-temperature regions
This project focuses on the development of an IoT-enabled hydroponic system designed to mitigate the effects of high ambient temperatures on plant growth and maintain optimal growing conditions, particularly in high temperature regions like Malaysia. By utilizing a Deep Water Culture (DWC) hydroponi...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
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| Online Access: | http://eprints.utar.edu.my/6860/1/3E_2101121_Final_report_%2D_SHI_JIAN_CHUA.pdf http://eprints.utar.edu.my/6860/ |
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| Summary: | This project focuses on the development of an IoT-enabled hydroponic system designed to mitigate the effects of high ambient temperatures on plant growth and maintain optimal growing conditions, particularly in high temperature regions like Malaysia. By utilizing a Deep Water Culture (DWC) hydroponic method, the system integrates sensors to monitor critical environmental parameters such as temperature, humidity, pH, and Total Dissolved Solids (TDS), ensuring optimal plant growing condition. A fogger system was
implemented to reduce temperature stress on plants during peak heat conditions. Experimental results demonstrated that the fogger system significantly improved the growth of lettuce plants, as evidenced by greater plant height and larger leaf area compared to those grown without the fogger
cooling mechanism.
Besides real-time environmental monitoring, the system utilizes machine learning models, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), to predict environmental conditions and optimize system performance through adaptive control. The dataset, collected
by the hydroponic system over a period of one month, was used to train these models. Comparative analysis showed that the GRU model performed slightly better in predictive accuracy. The integration of IoT and AI technologies into
hydroponic farming has the potential to transform agricultural practices by promoting sustainable and efficient crop production. This solution automates
environmental control, reduces the need for human intervention, and optimizes resource use, making it a promising approach for the future of modern agriculture.
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