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Interpretable Machine Learning for Multi-Dimensional Visual Quality Grading Under Small-Data Conditions: A Case Study on Artisanal Flatbread
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AI
DOI：10.3390/make8070195.png)
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Abstract
En 中文
Interpretable machine learning for ordinal quality grading faces a fundamental tension between model transparency and predictive performance, particularly under small-data conditions where end-to-end deep learning is unreliable and domain knowledge must compensate for limited training samples. We present a dual-target feature engineering framework for interpretable ordinal grading validated on pane Carasau, a traditional flatbread whose extreme surface variability makes it a challenging small-data benchmark for machine learning under realistic acquisition constraints. The pipeline extracts 116 handcrafted visual descriptors organised into four families—colour, texture, spatial, and hotspot—and grades the quality along two independent axes: global toasting intensity and spatial uniformity, complemented by a continuous Toasting Index for process monitoring, on a dataset of 1512 images spanning four acquisition campaigns and three product types. On the primary within-batch evaluation set Campaign 01, N = 1090 ), XGBoost achieves F1 macro = 0.906 for toast classification and R 2 = 0.886 for continuous regression, substantially outperforming two fine-tuned CNN baselines on the same evaluation set (MobileNetV2: F1 = 0.523 ; EfficientNet-B0: F1 = 0.518 ). Feature importance analysis reveals that colour descriptors dominate toasting prediction ( 87.5 % ), whilst spatial and texture features are essential for uniformity assessment ( 47.4 % combined), providing physically grounded explanations directly traceable to the underlying thermal process. Cross-batch generalisation on held-out campaigns is moderate for the same product (XGB F1 = 0.718, κ = 0.703); cross-product transfer to geometrically distinct variants requires product-specific adaptation. The framework requires no GPU, runs on standard CPU hardware at 4 s per image, and provides complete decision transparency, supporting deployment without specialised hardware.
Keywords:
interpretable machine learning
feature engineering
ordinal classification
small-data learning
visual quality inspection
explainable AI
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6
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