Design and fabrication of an automated vermicomposting machine using raspberry PI / Adrian C. Adamana, Jasmin V. Genovea, Jocelyn J. Jimenez, John Eman P. Libed, Dhezerieh Padilla & Frances Micah B. Rayoan
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TextPublication details: City of Batac: MMSU, 2024Description: xix, 99 leaves : 29 cmSummary: This thesis study, entitled Design and Fabrication of an Automated Vermicomposting Machine using Raspberry pi presents the development of a vermicomposting machine that automates the composting process. Traditional vermicomposting methods often require significant manual labor and monitoring to maintain optimal conditions for the worms. This project aims to address these challenges by integrating modern technology to enhance efficiency, productivity and offer greater convenience. The system was developed using different software and hardware resources. It features a web application for monitoring, providing real-time updates of the data acquired from the sensors. Key parameters such as temperature and moisture are continuously monitored and adjusted to ensure optimal composting conditions.
| Item type | Home library | Shelving location | Call number | Status | Notes | Date due | Barcode |
|---|---|---|---|---|---|---|---|
| Thesis/Dissertation | MMSU Main Library | Theses and Dissertation Section | Available | Room Use Only | UTHESIS-6922 |
UTHESIS ( Bachelor of Science in Computer Engineering)
Bibliography: leaves 75-79
This thesis study, entitled Design and Fabrication of an Automated Vermicomposting Machine using Raspberry pi presents the development of a vermicomposting machine that automates the composting process. Traditional vermicomposting methods often require significant manual labor and monitoring to maintain optimal conditions for the worms. This project aims to address these challenges by integrating modern technology to enhance efficiency, productivity and offer greater convenience. The system was developed using different software and hardware resources. It features a web application for monitoring, providing real-time updates of the data acquired from the sensors. Key parameters such as temperature and moisture are continuously monitored and adjusted to ensure optimal composting conditions.
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