Research Progress on Intelligent Color-Sorting Equipment for Post-Harvest Chili Peppers: Machine Vision, Pneumatic Actuation, and System Integration

Post-harvest sorting is essential for converting the biological variability of chili peppers into consistent commercial grades, efficient processing, and higher market value. Rapid advances in machine vision, multimodal sensing, deep learning, and intelligent actuation are transforming sorting equipment from rule-based classifiers into integrated perception-to-execution systems. However, existing studies often evaluate isolated algorithms or components, leaving limited evidence that recognition accuracy translates into reliable sorting during continuous operation. We conducted a structured narrative review of English-language studies published from January 2008 to July 2026 using Web of Science, Scopus, AESC, and PubMed. Evidence was synthesized along a perception-to-execution chain, distinguishing pepper-specific sorting systems and component studies from cross-crop engineering evidence. Direct system-level evidence was concentrated in a small number of chili and bell pepper studies, whereas much of the technical discussion drew on apple, tomato, potato, sweet potato, and onion research. Visible, spectral, fluorescence, and multimodal features can distinguish ripeness, color grades, and surface defects, although performance remains sensitive to cultivar, illumination, pose, and dataset design. A small number of pepper sorting studies support the feasibility of integrating vision, conveying, and physical separation under specific operating conditions. Cross-crop studies inform the discussion of localization, pneumatic actuation, and system integration, but do not establish the performance of these approaches in chili pepper sorting. Reported performance is difficult to compare because studies rarely standardize latency, target association, false and missed rejections, product damage, energy use, and long-term reliability. Robust deployment therefore requires cross-batch datasets, synchronized target-level traceability, and evaluation protocols linking perception outputs to final physical destinations. This review identifies the boundaries of current pepper-specific evidence and proposes a system-level evaluation framework and research priorities for testing the transferability of cross-crop engineering approaches to pepper sorting.