AI-based bread quality assessment using image processing techniques and the developed BQe-CNN

Abstract Ensuring consistent bread quality is critical for maintaining industry standards, reducing waste, and sustaining consumer satisfaction. Traditional quality assessment relies on expert visual inspection, which is subjective, time-consuming, and prone to inconsistency. While proprietary machine analysers exist, their high costs limit accessibility for many bakeries and research facilities. This study presents an artificial intelligence (AI)-driven solution that applies advanced image processing to automate and improve bread quality assessment. The Bread Quality Enhanced Convolutional Neural Network (BQe-CNN) analyzes features, such as porosity, texture, air cell structure, and expert-scored colour and crumb metrics. Achieving classification accuracies of 92% for bread colours and 88% for quality levels, the model offers reliable, cost-effective evaluation without requiring expensive equipment or specialised training. By integrating residual connections and attention mechanisms, BQe-CNN captures subtle variations in texture and air-cell distribution, outperforming conventional methods in precision and scalability. However, artisan scoring—valuing nuanced aesthetics central to traditional bread-making—remains challenging for full automation, highlighting opportunities for hybrid or more advanced approaches. Overall, BQe-CNN demonstrates the potential of AI-based image analysis to deliver real-time, standardised quality control. This innovation cuts variability, reduces waste, and provides computational precision with baking expertise—advancing bread quality management.