TY - JOUR
T1 - Automated tobacco grading using image processing techniques and a convolutional neural network
AU - Marzan, Charlie S.
AU - Ruiz, Conrado R.
N1 - Publisher Copyright:
© 2019 International Association of Computer Science and Information Technology.
PY - 2019
Y1 - 2019
N2 - Tobacco grading is very important for crop market price determination. It is beneficial for graders who need to manually classify tobacco leaves according to their grades. As such, a grading system using image processing techniques and a Convolutional Neural Network (CNN) is proposed in this study which aims to surpass existing results in tobacco grading. The system consists of image acquisition, pre-processing, leaf detection, segmentation, and classification. Tobacco leaf images were directly taken at the tobacco grading room and pre-processed for subsequent tasks. Through a Haar cascade classifier and applying image processing techniques, air-cured tobacco leaves are automatically detected and extracted in images. This method produced satisfactory results as it can successfully detect single and multiple tobacco leaves taken under different positions and scale. All detected tobacco leaves underwent various image processing to precisely segment leaves from the rest of the image. The experimental results also reveal that using segmented and nonsegmented images, CNN classifier can effectively grade tobacco leaves as high as 96.25% accuracy rate and on average, took 7.43 ms to classify a single tobacco leaf. This approach outperforms current methods in grading tobacco leaves.
AB - Tobacco grading is very important for crop market price determination. It is beneficial for graders who need to manually classify tobacco leaves according to their grades. As such, a grading system using image processing techniques and a Convolutional Neural Network (CNN) is proposed in this study which aims to surpass existing results in tobacco grading. The system consists of image acquisition, pre-processing, leaf detection, segmentation, and classification. Tobacco leaf images were directly taken at the tobacco grading room and pre-processed for subsequent tasks. Through a Haar cascade classifier and applying image processing techniques, air-cured tobacco leaves are automatically detected and extracted in images. This method produced satisfactory results as it can successfully detect single and multiple tobacco leaves taken under different positions and scale. All detected tobacco leaves underwent various image processing to precisely segment leaves from the rest of the image. The experimental results also reveal that using segmented and nonsegmented images, CNN classifier can effectively grade tobacco leaves as high as 96.25% accuracy rate and on average, took 7.43 ms to classify a single tobacco leaf. This approach outperforms current methods in grading tobacco leaves.
KW - Convolutional neural network
KW - Image processing techniques
KW - Leaf detection
KW - Tobacco grading
UR - http://www.scopus.com/inward/record.url?scp=85077556261&partnerID=8YFLogxK
U2 - 10.18178/ijmlc.2019.9.6.877
DO - 10.18178/ijmlc.2019.9.6.877
M3 - Article
AN - SCOPUS:85077556261
SN - 2010-3700
VL - 9
SP - 807
EP - 813
JO - International Journal of Machine Learning and Computing
JF - International Journal of Machine Learning and Computing
IS - 6
ER -