CC BY 4.0Marzan, Charlie S.Ruiz, Conrado Jr.Aran, Oya2026-07-202026-07-202026Marzan, 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.V18.13921793-8201e2972-4511https://lakasa.dmmmsu.edu.ph/handle/123456789/2229Grading 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.en-USTobacco leaf gradingDeep learningMulti-task learningComputer visionImage processingImage processingAgricultural automationMachine learning in agricultureBurley tobaccoArtificial intelligenceCrop quality assessmentComputer visionArtificial intelligenceMachine learningTobacco industry--Quality controlTobacco leaves--ClassificationComputer visionDeep learning (Artificial intelligence)Image processing--Digital techniquesPrecision agricultureArtificial intelligenceArticle