Instance-Level Maturity Recognition of Pleurotus citrinopileatus via Ordinal Learning and Morphology-Guided Feature Fusion

Accurate maturity recognition of Pleurotus citrinopileatus fruiting bodies is important for growth monitoring and harvest assistance in controlled cultivation. However, multiple developmental stages often coexist within one image, and near-mature fruiting bodies share visual characteristics with both adjacent stages. We developed a two-stage RGB-based framework for instance-level maturity recognition in complex cultivation scenes. After fruiting-body localization, each detected instance was represented by paired local and context views and classified using a DINOv3 ViT-S/16-based ordinal model. The task-specific methodological contribution is a morphology-guided fusion module that incorporates automatically extracted geometric, color, intensity, and edge descriptors into the dual-view visual representation. Object-level matching was used to quantify how detection and classification errors affected the complete pipeline, and temperature scaling was applied for probability calibration. Among five candidate detectors, RF-DETR-Small achieved validation mAP50 and mAP50–95 values of 0.9747 and 0.8813, respectively. On the independent ROI test set, the final classifier achieved an accuracy of 0.8779, a macro F1 of 0.7950, a near-mature F1 of 0.5546, and a QWK of 0.9068. Temperature scaling reduced ECE from 0.1094 to 0.0564 without changing the predicted labels. On 370 original cultivation images containing 475 ground-truth instances, the unfiltered pipeline achieved an object-level macro F1 of 0.7072 and a near-mature F1 of 0.4672. Candidate-box filtering increased these scores to 0.7305 and 0.4885, respectively, but increased the number of missed instances from 14 to 23. These results demonstrate the feasibility of non-destructive instance-level maturity monitoring while identifying near-mature discrimination and candidate-box control as priorities for practical application.