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

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

Open Access
|Dec 2019

Figures & Tables

Figure 1.

Illustration of tobacco cultivation (FAO, 2018a).

Figure 2.

Tobacco leaf categories based on leaf position.

Figure 3.

Leaf defect categories based on holes or torn positions.

Figure 4.

Mature/ripe leaf subcategories based on the color of tobacco leaf.

Figure 5.

The procedure of leaf area segmentation.

Figure 6.

Area of leaf defects category.

Figure 7.

Small patches applied on the leaf area.

Figure 8.

A set of the threshold to determine the color category.

Figure 9.

A set of the threshold to determine the class in each color category.

Figure 10.

The threshold of hue pixel to segment leaf area.

Figure 11.

The result of a tobacco leaf area segmentation.

Figure 12.

The result of leaf defects detection.

Table 1.

The threshold for local color-based classification.

ThresholdHueValue
Lower1560
Middle34127
Upper80200
Figure 13.

The distribution of hue-value features of a color-based tobacco leaf category.

Figure 14.

The result of color-based tobacco leaf classification.

Table 2.

Performance evaluation of color-based tobacco leaf classification.

Actual
MKB
M3312
PredictedK8724
B2082
Accuracy0.91667
Precision0.91667
Sensitivity0.89519
Specificity0.95751
Language: English
Page range: 1 - 10
Submitted on: Jan 30, 2019
Published on: Dec 16, 2019
Published by: International Journal on Smart Sensing and Intelligent Systems
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Agus Harjoko, Adhi Prahara, Tri Wahyu Supardi, Ika Candradewi, Reza Pulungan, Sri Hartati, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.