Automated detection of defective coffee beans based on improved YOLOv10 framework

Coffee quality assessment is critical to the global agricultural economy, yet traditional manual inspection remains labor-intensive, subjective, and prone to inconsistency. This study presents an improved YOLOv10-based framework specifically engineered for the automated detection of defective green coffee beans, achieving an optimal balance between high-precision localization and real-time edge deployment constraints. The proposed architecture integrates three targeted structural innovations: Depthwise Separable Convolution (DSConv) with distribution shift for lightweight, fine-grained feature extraction; a Spatial Pyramid Pooling Fast with Attention (SPPF_Attention) module for context-aware multi-scale feature refinement; and Partial Convolution (PConv) operators in the detection head to mitigate computational redundancy and enhance robustness against dense occlusions. Comprehensive evaluations utilized a rigorously curated dataset covering seven defect categories compliant with Specialty Coffee Association (SCA) standards. Evaluated under a 5-fold cross-validation protocol, the framework achieved state-of-the-art performance with 99.2% mAP@50, 98.5% precision, and 98.8% recall, alongside a rapid inference latency of merely 2.0 ms. These metrics denote substantial improvements over the advanced YOLOv11 baseline, simultaneously reducing model parameters by 21.6%, decreasing model size by 12.4%, and accelerating inference by 33.3%. Furthermore, systematic ablation studies validate the incremental contributions of each architectural component, while EigenCAM visualizations confirm that the integrated attention mechanisms successfully steer the network toward semantically meaningful defect regions and suppress background interference. Extensive cross-dataset analyses additionally demonstrate the model's exceptional generalization capability across diverse processing states, establishing this framework as a highly viable, scalable solution for resource-constrained industrial sorting facilities.