Applications Progress on Machine Vision Technology in the Entire Industrial Chain of Traditional Chinese Medicinal Materials
Machine vision technology (MVT) has emerged as a pivotal non-contact, high-efficiency, and low-cost sensing modality for the traditional Chinese medicinal materials (TCMM) industry. This review systematically examines MVT applications across the entire TCMM industrial chain, spanning seed quality evaluation and sorting, cultivation environment suitability analysis and zoning, plant growth monitoring and field management, harvesting decision-making, primary processing control, and product quality assessment. By integrating conventional visual imaging with multi-source spectral imaging and employing both traditional machine learning and deep learning algorithms, MVT has substantially enhanced production intelligence and quality control capabilities. Nevertheless, persistent challenges impede industrial-scale adoption: technological fragmentation, data silos, lab-bound models with poor field generalizability, and prohibitive implementation costs. Guided by China's 'Artificial Intelligence Plus' strategy, this paper proposes strategic directions including establishing intelligent agent systems covering the full industrial chain, creating collaborative data resource ecosystems, developing lightweight adaptive algorithm models, and innovating inclusive business models. These measures aim to promote deep integration and large-scale application of MVT in the TCMM industry, providing technical support for intelligent, high-quality development of the entire value chain.