An Energy-Efficient Edge Vision System for Front-End Quality Monitoring in Sustainable Cold Chains: Coffee Fruit Ripeness Detection and Embedded Validation

Reliable quality monitoring at the front end of sustainable cold chains requires direct observations of product condition in addition to environmental measurements. This study developed an energy-efficient edge vision system for six coffee fruit ripeness and visible-condition classes on an RK3576 neural processing unit (NPU). YOLO11 was reconfigured by combining a Branch-Integrated Feature Aggregation module for fine-grained representation, a YOLOv10-derived Cross-Stage Partial Network with Compact Inverted Block for efficient multi-scale fusion, and a Simplified Regression Head with reduced regression channels. On a public benchmark derived from 996 source images, YOLO11 and YOLO11BCS yielded mean average precision values at an intersection-over-union threshold of 0.5 (mAP50) of 81.5% and 83.2%, respectively, under the matched 320-pixel protocol. At a 640-pixel input, the computational complexity decreased from 6.3 to 5.5 giga floating-point operations. The largest class-level mAP50 difference was 9.7 percentage points for overripe fruit. Under controlled synthetic degradations, the model produced higher point estimates than YOLO11 for illumination variation and rain, whereas severe Gaussian noise and motion blur remained limiting. Board-input measurements showed throughput increases of 24.2% and 22.1% at 640- and 320-pixel inputs, respectively. The 320-pixel system reached 50.3 frames per second and 10.06 frames per second per watt. Within the supplied benchmark, the matched evaluation supports a comparative assessment of the models, while source-image correspondences across partitions define the scope of the reported absolute metrics. Within these boundaries, the results support low-power visual quality monitoring near the point of harvest.