| 基于机器视觉的葡萄品质无损检测方法研究进展 |
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| 引用本文： | 刘云玲,张天雨,姜明,李勃,宋坚利.基于机器视觉的葡萄品质无损检测方法研究进展[J].农业机械学报,2022,53(S1):299-308. |
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| 作者姓名： | 刘云玲 张天雨 姜明 李勃 宋坚利 |
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| 作者单位： | 中国农业大学;中国农业大学烟台研究院;山东省葡萄研究院 |
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| 基金项目： | 国家精准农业应用项目（JZNYYY001） |
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| 摘 要： | 我国葡萄产量逐年上升，田间葡萄品质检测有益于提高葡萄收获后流入市场的经济效益。传统田间葡萄品质检测主要依靠人工进行破坏性检测，存在经验差异导致的误差。随着深度学习、图像检测技术的发展，基于机器视觉的田间葡萄品质检测克服了传统人工检测的局限性，以快速精准、实时无损检测的优势得到了大量应用。葡萄品种不同，衡量其内、外在品质评级的指标也不同。本文根据葡萄品种与品质评价指标，从品种的机器视觉检测方法、品质的机器视觉检测方法展开，对国内外基于机器视觉技术的田间葡萄品质无损检测相关研究进行系统性分析与总结。总结了不同机器视觉检测方法对葡萄品质指标检测的优缺点，并对田间葡萄品质无损检测研究面临的问题进行了讨论，指出了今后的发展趋势与研究方向。
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| 关 键 词： | 田间葡萄 深度学习 无损检测 机器视觉 |
| 收稿时间： | 2022/6/7 0:00:00 |
Review on Non-destructive Detection Methods of Grape Quality Based on Machine Vision |
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| LIU Yunling,ZHANG Tianyu,JIANG Ming,LI Bo,SONG Jianli.Review on Non-destructive Detection Methods of Grape Quality Based on Machine Vision[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(S1):299-308. |
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| Authors: | LIU Yunling ZHANG Tianyu JIANG Ming LI Bo SONG Jianli |
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| Abstract: | As the grape production increases year by year, the quality detection of grapes in the field becomes more and more important to improve the economic benefits after flowing into the market. The traditional method of external quality detection, which mainly relies on the observation of workman, introduces non-negligible errors. The intrinsic quality detection is considered as destructive and inefficient by using the method of sugar level testing of grapes. With the development of deep learning and image processing technology, the field grape quality detection based on machine vision overcomes the limitations of traditional manual inspection and has the advantages of fast, accurate, real time and lossless. According to grape varieties and quality evaluation indicators, a systematical analysis and summary of the research related to the non destructive quality detection method of grapes in the field was provided based on machine vision technology. The main body consisted of two parts, which were machine vision detection methods of grape varieties and machine vision detection methods of grape quality. The common factors affecting the quality of grapes were obtained on the basis of the analysis of different grape variety evaluation factors. The intrinsic quality factors included soluble solids, total acid, total phenol and moisture content while the external quality factors included fruit size, quantity, color, and disease defects and so on. Several methods of grape variety identification based on fruit and leaf were introduced, including canonical correlation analysis, support vector machine, and deep learning. The detection method based on fruit characteristics was more accurate, while the detection method based on leaf characteristics can be applied to a longer growth period. As the variety of grapes differred, the standard of their internal and external quality also varied. A detailed summary of the research related to the non-destructive quality detection methods for the intrinsic quality and external quality of grapes in the field was provided. For the quality detection of grapes, the comparison was conducted between the traditional morphological methods such as thresholding, the edge contour search and the corner detection algorithm with the deep learning methods such as Mask R-CNN. It was concluded that the deep learning detection method held the advantages of strong scalability, fast detection speed and high accuracy. In addition, the application principle and advantages and disadvantages of near infrared spectroscopy and hyperspectral imaging technology in intrinsic quality detection were summarized. Hyperspectral technology outperformed in terms of accuracy, while nearinfrared spectroscopy technology had lower cost and faster analysis speed. In the field of non-destructive quality detection of grapes, machine vision algorithms based on spectral analysis still faced the challenges of complex field grape growth environment and variable daytime light. Finally, in view of the difficulty of image acquisition, insufficient multi dimensional image information, and weak foundation of detection instruments faced by non destructive quality detection methods of grapes in the field, it was proposed that it was necessary to improve the intelligent equipment for data collection and analysis while improving the machine vision algorithm, thus providing efficient tools combining software and hardware for the quality detection of grapes in the field. |
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| Keywords: | field grape deep learning non-destructive detection machine vision |
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