Automatic mango detection using image processing and HOG-SVM
College
College of Computer Studies
Department/Unit
Software Technology
Document Type
Conference Proceeding
Source Title
ACM International Conference Proceeding Series
First Page
211
Last Page
215
Publication Date
12-14-2018
Abstract
Mango is an agricultural produce with high export value as it is being consumed internationally. To ensure its production yield, the manual handling and classification tasks should be performed with precision and care by local farmers. Image processing and machine learning has improved the way classification, defect detection, and yield approximation are handled. Detection is considered as an initial step prior to performing these tasks. This paper presents an automatic mango detector by combining a Support Vector Machine (SVM) classifier trained with Histogram of Oriented Gradients (HOG) features and image segmentation. The image segmentation performed on both HSV and RGB color spaces using image processing techniques achieved a mean IoU of 0.7938. A HOG-SVM based classifier was trained and achieved an F-score of 89.38%. Results show that combining segmentation with HOG-SVM can detect and localize healthy and defective mango images with different background color and illumination. © 2018 Association for Computing Machinery.
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Digitial Object Identifier (DOI)
10.1145/3301326.3301358
Recommended Citation
Baculo, M. C., & Marcos, N. (2018). Automatic mango detection using image processing and HOG-SVM. ACM International Conference Proceeding Series, 211-215. https://doi.org/10.1145/3301326.3301358
Disciplines
Computer Sciences | Software Engineering
Keywords
Image processing; Image converters; Mango—Grading--Automation
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