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

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.