Automatic mango detection using Image processing and HOG-SVM
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Date
2018-12
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Computing Machinery
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.
Description
Full text.
Keywords
Image processing, Computer vision, Histogram of Oriented Gradients, Support Vector Machine, Object detection, Agricultural automation, Mango detection, Fruit grading
Citation
Baculo, M. J. 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
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