<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Implementation of automation technology for grading tobacco leaf was very promising. In Indonesia, grading tobacco leaf was done manually and relied on the skill and experience of tobacco leaf graders. Large tobacco plantation needed many graders, and the workers needed to be trained, to become a skilled grader. It would take a long time and substantial cost to prepare sufficient graders. Even if the plantation had enough graders, monotonous and long duration of work would raise the human error. Therefore, we proposed a method for grading tobacco leaf based on color and quality using image processing techniques. This work covered quality inspection of tobacco leaf, namely leaf defect detection and classification of tobacco leaf based on color. Image processing techniques such as image thresholding, morphological operation, blob detection, and color analysis of tobacco leaf were employed to determine the grade of tobacco leaf. From the experiment, the proposed method was able to detect a leaf defect and able to classify tobacco leaf with 91.667% accuracy.</p>
</abstract>

Implementation of automation technology for grading tobacco leaf was very promising. In Indonesia, grading tobacco leaf was done manually and relied on the skill and experience of tobacco leaf graders. Large tobacco plantation needed many graders, and the workers needed to be trained, to become a skilled grader. It would take a long time and substantial cost to prepare sufficient graders. Even if the plantation had enough graders, monotonous and long duration of work would raise the human error. Therefore, we proposed a method for grading tobacco leaf based on color and quality using image processing techniques. This work covered quality inspection of tobacco leaf, namely leaf defect detection and classification of tobacco leaf based on color. Image processing techniques such as image thresholding, morphological operation, blob detection, and color analysis of tobacco leaf were employed to determine the grade of tobacco leaf. From the experiment, the proposed method was able to detect a leaf defect and able to classify tobacco leaf with 91.667% accuracy.

Image processing approach for grading tobacco leaf based on color and quality

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

running-head

Implementation of automation technology for grading tobacco leaf was very promising. In Indonesia, grading tobacco leaf was done manually and relied on the...

Threshold	Hue	Value
Lower	15	60
Middle	34	127
Upper	80	200

			Actual
		M	K	B
	M	33	1	2
Predicted	K	8	72	4
	B	2	0	82
Accuracy	0.91667
Precision	0.91667
Sensitivity	0.89519
Specificity	0.95751

Image processing approach for grading tobacco leaf based on color and quality

Article Category: research-article

Data publikacji: 16 gru 2019

Zakres stron: 1 - 10

Otrzymano: 30 sty 2019

DOI: https://doi.org/10.21307/ijssis-2019-010

Słowa kluczowe
Tobacco leaf, Grading system, Image processing, Color, Quality

© 2019 Agus Harjoko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The threshold for local color-based classification.

Performance evaluation of color-based tobacco leaf classification.

Image processing approach for grading tobacco leaf based on color and quality

Article Category: research-article

Data publikacji: 16 gru 2019

Zakres stron: 1 - 10

Otrzymano: 30 sty 2019

DOI: https://doi.org/10.21307/ijssis-2019-010

Słowa kluczoweTobacco leaf, Grading system, Image processing, Color, Quality

© 2019 Agus Harjoko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

Figure 12.

Figure 13.

Figure 14.

The threshold for local color-based classification.

Performance evaluation of color-based tobacco leaf classification.

Słowa kluczowe
Tobacco leaf, Grading system, Image processing, Color, Quality