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

• Main Authors: Marzan, Charlie S., Marcos, Nelson
• Format: text
• Published: Animo Repository 2018
• Subjects: Image processing; Image converters; Tobacco—Grading; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3415; https://animorepository.dlsu.edu.ph/context/faculty_research/article/4417/type/native/viewcontent/3282286.3282292
• Institution: De La Salle University

Tobacco 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. © 2018 Association for Computing Machinery.