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dc.contributor.authorBarcelon, Menkent S.
dc.contributor.authorOrilla, Alvin A.
dc.contributor.authorMahilum, Jessabelle A.
dc.date.accessioned2026-05-06T03:17:32Z
dc.date.available2026-05-06T03:17:32Z
dc.date.issued2019
dc.identifier.issn21531633
dc.identifier.urihttps://repository.umindanao.edu.ph/handle/123456789/2303
dc.identifier.uri10.1145/3383783.3383786
dc.descriptionThis study develops an automated vermicomposting system using Arduino and Raspberry Pi to monitor and maintain optimal conditions for efficient, reduced-labor vermicast production.en_US
dc.description.abstractComposting of organic waste is an efficient technology to convert organic wastes into useful composts used as biofertilizers for sustainable agriculture. Vermicomposting allows the process of breaking down biodegradable matter with the help of earthworms that transforms the nutrients of the organic matter to vermicast. This study aimed to provide an automated system for vermicast production that may improve the whole concept of organic farming. Developing a system that automatically examines a worm bin, which is used to produce vermicast or supports the process of vermicomposting. Worms and substrate are used for the preparation of the worm bin, making the sensors applicable afterward. Two Arduino microcontrollers and Raspberry Pi capture the data readings from the sensors. The second Arduino microcontroller controls the maintenance of the worm environment and switches for the activation/deactivation of the system, and the data gathered are stored in Raspberry Pi. By the span of fourteen, sixteen, and nineteen days only, the conducted experiment still produces an adequate nutrient and compost quality for a fertilizer. Sensor readings with water sprinkler system combinations do maintain the right environment for the living conditions of the worm. Through the use of the microcontrollers, Arduino and Raspberry Pi, human intervention would reduce, and the system would expedite if the process of vermicomposting would be automated rather than going manual. © 2019 ACM.en_US
dc.description.sponsorshipSeoul National University; Sun Yat-Sen Universityen_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.subjectVermicultureen_US
dc.subjectVermicompostingen_US
dc.subjectMonitoring systemen_US
dc.subjectArduinoen_US
dc.subjectRasberryPien_US
dc.titleAutomated vermiculture monitoring and compost segregating system using microcontrollersen_US
dc.typeOtheren_US


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