Siamese networks for few-shot coffee bean defect detection

Defective coffee beans compromise flavor profiles and product quality, necessitating the real-time identification of defects throughout the supply chain. While convolutional neural networks have advanced agricultural image recognition, they remain limited in few-shot classification of fine-grained defects due to dependence on large annotated datasets. This study presents a Siamese neural network approach for few-shot coffee bean defect detection through similarity-based learning. The architecture employs dual ResNet18-based branches (adapted for single-channel 224 × 224 input, outputting 512-dimensional features) with shared weights, utilizing Euclidean distance for feature matching. A dataset of 3220 images encompassing sound beans and six defect categories was constructed from samples in Yunnan Province. The model achieved 94.95 % accuracy, substantially outperforming traditional CNN (74.35 %) and support vector machine (64.28 %) approaches. The processing speed of 34 ms per image enables real-time industrial deployment. This approach demonstrates robust generalization under limited sample conditions, offering practical value for resource-constrained production environments in coffee supply chains while reducing reliance on extensive labelled datasets.