Towards tobacco leaf detection using Haar cascade classifier and image processing techniques

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creativework.keywordsHaar cascade classifier; image processing techniques; leaf detection; tobacco grading
dc.contributor.authorMarzan, Charlie S.
dc.contributor.authorMarcos, Nelson
dc.date.accessioned2026-08-19T16:10:43Z
dc.date.available2026-08-19T16:10:43Z
dc.date.issued2018-10
dc.descriptionFull text
dc.description.abstractTobacco grading needs an effective leaf detection algorithm to ensure accurate results in segmentation and feature extraction. Leaf detection in this research used Haar cascade classifier and image processing techniques to automatically detect tobacco leaves in images. The proposed detection algorithm was implemented through OpenCV Python. The Haar cascade classifier was trained with 1,000 images and tested with 150 images. To improve the detection results of the classifier and ultimately detecting tobacco leaves, image processing techniques such as converting RGB to grayscale, blurring, thresholding, and finding connected components were applied. The experimental results show that the classifier can successfully distinguish tobacco leaves from other objects even those having resemblance to the characteristics of tobacco leaves in terms of color and shape. The accuracy rate of at least 91.33% proves the capability of the Haar cascade classifier to detect single and multiple tobacco leaves posed at different angles and taken at different distances from the camera. After applying some image processing techniques, the detection rate reached 100.00% and took 62 ms on average.
dc.identifier.citationMarzan, C. S., & Marcos, N. (2018). Towards tobacco leaf detection using Haar cascade classifier and image processing techniques. In Proceedings of the 2018 International Conference on Graphics and Signal Processing (pp. 63–68). Association for Computing Machinery. https://doi.org/10.1145/3282286.3282292
dc.identifier.doihttps://doi.org/10.3303/CET1756042
dc.identifier.isbn978-1-4503-6386-0
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2392
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.urihttps://dl.acm.org/doi/10.1145/3282286.3282292
dc.sdgSDG 9
dc.sdgSDG 8
dc.subjectHaar cascade classifier
dc.subjectImage processing techniques
dc.subjectLeaf detection
dc.subjectTobacco grading
dc.subjectObject detection
dc.subjectComputer vision
dc.subjectOpenCV Python
dc.subject.ddcComputer vision
dc.subject.ddcOptical pattern recognition
dc.subject.ddcTobacco production – - Data processing
dc.subject.ddcComputer applications
dc.subject.lcshComputer vision
dc.subject.lcshImage processing -- Digital techniques
dc.subject.lcshPattern recognition systems
dc.subject.lcshObject recognition (Computer science)
dc.subject.lcshTobacco -- Grading
dc.subject.lcshAgricultural automation
dc.titleTowards tobacco leaf detection using Haar cascade classifier and image processing techniques
dc.typeArticle
oaire.citation.endPage68
oaire.citation.startPage63
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