Identifying poor-laying hens is important for reducing feed waste, but visual screening is labor-intensive. Motivated by the relationship between laying status and pigmentation, we propose CHIC (Color-Histogram Integrated Classifier), a lightweight model combining HVI (Horizontal, Vertical, Intensity) transformation with differentiable histogram encoding of learned feature activations and global-average-pooled features. Evaluation used a four-class head-and-leg patch dataset collected from 20 hens in six cages at a single facility, with cage-level laying-status labels. Across ten training runs with different development-data folds and a fixed frame-level test set, CHIC achieved mean binary F1-scores of 92.21% for head patches and 96.06% for leg patches, exceeding the MobileNetV4-S baselines. With 2.69 million parameters, the classifier had a mean forward-pass latency of 21.87 ms per patch on an NVIDIA Jetson Orin Nano Super. Component comparisons showed configuration-dependent effects; histogram integration also benefited grayscale inputs, while mean–standard-deviation pooling with HVI inputs achieved similar mean performance to CHIC. The classification advantage was maintained when predictions from detected head and leg regions were aggregated into hen-level decisions. However, performance declined substantially on unseen cages. These findings support CHIC as a candidate for automated poor-laying hen screening, with further validation required across independent hens and farms.