Multi-task deep learning for automated tobacco leaf grading in a controlled environment
Loading...
Date
2026
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Computer Theory and Engineering
Abstract
Grading tobacco leaves is crucial for ensuring fair pricing and quality control, however, the process is still largely carried out manually, resulting in a slow, subjective, and often inconsistent outcome. In this work, we present a multi-task deep learning approach designed to automate the grading of air-cured Burley tobacco leaves in controlled settings. The model is constructed with shared convolutional layers and separate task-specific branches, allowing it to predict stalk group, quality, and color at the same time, in line with the hierarchical grading system. To improve consistency, images were preprocessed using coin-based size normalization, rotation alignment, and segmentation. In our experiments, the multi-task model with EfficientNetB0 achieved an accuracy of 94.82% and significantly outperformed the multi-class and single-task baselines, while reducing both training time and inference delay. These findings suggest that multi-task learning can be a valuable and robust method for automated tobacco grading, showing gains in accuracy, speed, and scalability compared to other algorithms.
Description
Keywords
Tobacco leaf grading, Deep learning, Multi-task learning, Computer vision, Image processing, Image processing, Agricultural automation, Machine learning in agriculture, Burley tobacco, Artificial intelligence, Crop quality assessment
Citation
Marzan, C. S., Ruiz, C. Jr., & Aran, O. (2026). Multi-task deep learning for automated tobacco leaf grading in a controlled environment. International Journal of Computer Theory and Engineering, 18(2), 99-109. DOI: 10.7763/IJCTE.2026.V18.1392