【摘要】 在线感知技术包括一系列传感器模式、成像技术和智能数据处理算法等，已成为确保农产品质量、安全和适销性的关键工具。本文对该领域的最新研究进展进行了全面总结，重点关注在线感知系统在农产品品质中的应用，并系统分析了在线感知技术，包括机器视觉、高光谱成像、近红外光谱、荧光传感、3D深度传感、算法及数据分析等。讨论传感器硬件演变、机器学习和深度学习算法开发，以及用于实时质量控制的多模态数据集成。介绍了不同类别的具体应用示例，如水果和蔬菜分拣、谷物质量监测以及肉类和乳制品的评估。指出了未来趋势及新兴技术在革新农业质量感知技术方面的潜力。综上所述，在线感知技术在提升质量评估准确性与效率的同时，有助于减少浪费、保障安全合规，促进农业可持续发展。更多还原

【Abstract】 The agricultural industry has witnessed transformative advances with the integration of digital technologies into quality assessment and processing. Online perception technology, which encompasses an array of sensor modalities, imaging techniques, and intelligent data processing algorithms, has emerged as a pivotal tool in ensuring the quality, safety, and marketability of agricultural products. A comprehensive survey of recent research progress in the field was provided, focusing on the application of online perception systems for agricultural product quality assessment. The technological components underpinning these systems were critically analyzed, including machine vision, hyperspectral imaging, near-infrared spectroscopy, and advanced data analytics. The discussion covered the evolution of sensor hardware, algorithmic developments in machine learning and deep learning, and the integration of multi-modal data for real-time quality control. Specific application examples were presented across different categories, such as fruit and vegetable sorting, grain quality monitoring, and the evaluation of meat and dairy products. The future trends and the potential of emerging technologies were also outlined to further revolutionize the perception of agricultural quality perception technologies. In summary, it highlighted how online perception technologies not only enhanced the precision and efficiency of quality assessment but also contributed to sustainable practices by reducing wastage and ensuring compliance with safety standards.更多还原