Vachmanus S.Pridasawas W.Kusakunniran W.Thamrongaphichartkul K.Phinklao N.Mahidol University2026-02-062026-02-062026-03-01Intelligent Systems with Applications Vol.29 (2026)26673053https://repository.li.mahidol.ac.th/handle/123456789/114424In major training and export markets, the coffee bean grading process still relies heavily on manual labor to sort individual beans from large harvest volumes. This labor-intensive task is time-consuming, costly, and prone to human error, especially within Thailand's rapidly expanding Robusta coffee sector. This study introduces AL–ViT, an end-to-end Active-Learning Vision Transformer framework that operationalizes active learning and transformer-based feature extraction within a single, production-oriented pipeline. The framework integrates a ViT-Base/16 backbone with seven active learning (AL) query strategies, random sampling, entropy-based selection, Bayesian Active Learning by Disagreement (BALD), Batch Active Learning by Diverse Gradient Embeddings (BADGE), Core-Set diversity sampling, ensemble disagreement, and a novel hybrid uncertainty–diversity strategy designed to balance informativeness and representativeness during sample acquisition. A high-resolution dataset of 2098 Robusta coffee bean images was collected under controlled-lighting conditions aligned with grading-machine setups, with only 5 % initially labeled and the remainder forming the AL pool. Across five random seeds, the hybrid strategy without MixUp augmentation achieved 97.1 % accuracy and an F1bad of 0.956 using just 850 labels (41 % of the dataset), within 0.3 percentage points of full supervision. Operational reliability, defined as 95 % accuracy, consistent with prior inspection benchmarks, was reached with only 407 labels, reflecting a 75 % reduction in annotation. Entropy sampling showed the fastest early-stage gains, whereas BADGE lagged by >1 pp; Core-Set and Ensemble provided moderate but stable results. Augmentation and calibration analyses indicated that explicit methods (MixUp, CutMix, RandAugment) offered no further benefit, with the hybrid pipeline already achieving well-calibrated probabilities. Statistical validation via paired t-tests, effect sizes, and bootstrap CIs confirmed consistent improvements of uncertainty-driven strategies over random sampling. Overall, the proposed AL–ViT framework establishes a label-efficient and practically deployable approach for agricultural quality control, achieving near-supervised accuracy at a fraction of the labeling cost.Computer ScienceArticleSCOPUS10.1016/j.iswa.2025.2006122-s2.0-105024239946