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dc.contributor.advisorRichard Vincent E. Misa, MIT
dc.contributor.authorCocoy, Kristian Jay P.
dc.contributor.authorOcio, Mary Kareen Joy B.
dc.contributor.authorBaloyo, Aldwin Ray O.
dc.date.accessioned2025-03-17T12:46:10Z
dc.date.available2025-03-17T12:46:10Z
dc.date.issued2022-12
dc.identifier.urihttps://repository.umindanao.edu.ph/handle/20.500.14045/1504
dc.descriptionIn Partial Fulfillment of the Requirements for the Degree Bachelor of Science in Computer Scienceen_US
dc.descriptionIncludes bibliographic references
dc.description.abstractThe Philippine culture and livelihood agricultural sector are significant, particularly in rice production. However, rice paddy fields are now under the alert to vanish because of monetary setbacks, along with unguided bad chemical application practices that may result in poor harvest and environmental hazards. To alleviate these problems, countless research has been commenced that birthed precision agriculture. From extensive mechanical machinations to small gadgets like smartphones are utilized to ease farmers' labor. Diseases are a problem for farmers and have persisted for centuries until this generation. Image processing, in particular, has been a trend in detecting rice diseases in the 21st century. The researchers in this study plan to utilize TensorFlow Lite image classification as another method in image processing, along with treatment suggestions and guidance for farmers.en_US
dc.language.isoen_USen_US
dc.publisherDepartment of Arts and Sciences Education- Bachelor of Science in Computer Scienceen_US
dc.rightsUM Tagum College LIC
dc.subjectImage processingen_US
dc.titleRice paddy disease detection using image processingen_US
dc.typeThesisen_US
dc.contributor.panelBenjamin M. Mahinay Jr., MIT
dc.contributor.panelJohn Jefferson Dela Cruz, MIT
dc.description.xtntvi, 21 pages


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