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Article(id=1325194224307679506, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325194210151891078, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2024.03.007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1717171200000, receivedDateStr=2024-06-01, revisedDate=1722096000000, revisedDateStr=2024-07-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1793880523993, onlineDateStr=2026-11-05, pubDate=1723651200000, pubDateStr=2024-08-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793880523993, onlineIssueDateStr=2026-11-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793880523993, creator=13701087609, updateTime=1793880523993, updator=13701087609, issue=Issue{id=1325194210151891078, tenantId=1146029695717560320, journalId=1319690209941618849, year='2024', volume='5', issue='3', pageStart='1', pageEnd='74', issueExtLink='null', onlineDate='null', pubDate='1723651200000', pubDateStr='2024-08-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793880520618, creator='13701087609', updateTime=1793943985644, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325460402636719047, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325194210151891078, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325460402636719048, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325194210151891078, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=63, endPage=74, ext={EN=ArticleExt(id=1325194224823578899, articleId=1325194224307679506, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Development status and prospect of cold chain logistics of fruits, vegetables, and agricultural products based on intelligent technology, columnId=0, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=, runingTitle=null, highlight=null, articleAbstract=

The cold chain in the production area of fruits and vegetables is the primary link to reduce product loss and improve product quality, but it is also a weak link. With the application of big data technology in cold chain logistics, intelligent devices, and technologies have become important carriers for improving the efficiency of cold chain logistics in fruit and vegetable production areas, extending the shelf life of fruits and vegetables, and reducing fruit and vegetable losses. They have many advantages in fruit and vegetable pre-cooling, sorting and packaging, testing, warehousing, transportation, and other aspects. This article summarizes the rapidly developing and widely used intelligent technologies at home and abroad in recent years, including automated guided vehicle intelligent handling based on electromagnetic or optical technology, intelligent sorting based on sensors, electronic optics, and other technologies, intelligent detection based on computer vision technology, intelligent transportation based on perspective imaging technology, etc. It analyses and studies the innovative research and achievements of various scholars in applying intelligent technology in fruit and vegetable cold chain storage, sorting, detection, transportation, and other links, and improves the efficiency of fruit and vegetable cold chain logistics. However, applying intelligent technology in fruit and vegetable cold chain logistics also faces many problems. The challenges of high cost, difficulty in technological integration, and talent shortages have limited the development of intelligent technology in the field of fruit and vegetable cold chains. To solve the current problems, it is proposed that costs be controlled through independent research and development, technological innovation, and other means to lower the entry threshold for small enterprises. Strengthen integrating intelligent technology and cold chain logistics systems to improve data security and system compatibility. At the same time, the government should introduce relevant policies, provide necessary financial support, and establish talent training mechanisms. Accelerate the development and improvement of intelligent technology standards in the field of cold chain logistics. Through technological innovation, cost control, talent cultivation, and policy guidance, we aim to promote the upgrading of the agricultural industry and provide ideas for improving the quality and efficiency of fruit and vegetable cold chain logistics.

, authors=Ruizhi ZHANG1, Qianyuan LIU1, *, Yuying HUANG1, Bing LIU1, Zhiguang CHANG1, Jiaoling WANG2, 3, authorsList=Ruizhi ZHANG, Qianyuan LIU, Yuying HUANG, Bing LIU, Zhiguang CHANG, Jiaoling WANG, authorCompany=null, correspAuthors=Qianyuan LIU, authorNote=

ZHANG Ruizhi, engineer; research interests: agricultural engineering consulting and planning design. E-mail:

, correspAuthorsNote=
* LIU Qianyuan, engineer; research interests: agricultural engineering consulting and planning design. E-mail:
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=814NWccnW3CBAYNZB12PmQ==, magXml=jd7SALcadK5geenLAc4CsA==, pdfUrl=null, pdf=RytVr83dqudZPKObpgyrKg==, pdfFileSize=967920, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=JvampMy4ctOltBtD2/p7Qw==, mapNumber=null, fund=null), CN=ArticleExt(id=1325194224953602324, articleId=1325194224307679506, tenantId=1146029695717560320, journalId=1319690209941618849, language=CN, title=基于智能化技术的果蔬农产品产地冷链物流发展现状与展望, columnId=null, journalTitle=智能化农业装备学报（中英文）, columnName=null, runingTitle=null, highlight=null, articleAbstract=

果蔬产地冷链是降低产品损耗、提高产品品质的首要环节，更是薄弱环节。随着大数据技术在冷链物流中的应用，智能化设备与技术已成为提高果蔬产地冷链物流效率、延长果蔬保鲜期、降低果蔬损耗的重要载体，在果蔬预冷、分拣包装、检测、仓储、运输等方面有着诸多优势。本文通过研究总结国内外近几年发展迅速、使用广泛的智能化技术，包括以电磁或光学技术为基础的AGV智能搬运，以传感器、电子光学等技术为基础的智能分拣，以计算机视觉技术为基础的智能检测，以透视成像技术为基础的智能运输等，分析研究各学者在果蔬冷链仓储、分拣、检测、运输等环节中智能化技术应用的创新研究及取得的成果，提高了果蔬冷链物流效率。然而，智能化技术在果蔬冷链物流中的应用也面临诸多问题。成本高、技术融合难度高和人才短缺等挑战，使得智能化技术在果蔬冷链领域的发展受到一定限制。为解决当前存在的问题，提出通过自主研发、技术创新等手段进行成本控制，降低小型企业进入门槛。加强智能化技术与冷链物流系统的集成，提高数据安全性和系统兼容性。同时，政府应出台相关政策，提供必要的资金支持和人才培养机制。加快制定和完善冷链物流领域的智能化技术标准。通过技术创新、成本控制、人才培养和政策引导，推动农业产业升级，为果蔬冷链物流提质增效提供思路。

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基于智能化技术的果蔬农产品产地冷链物流发展现状与展望
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张睿智 1 , 刘倩媛 1, * , 黄雨婴 1 , 刘冰 1 , 常志光 1 , 王教领 2, 3
智能化农业装备学报（中英文） | 2024,5(3): 63-74
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智能化农业装备学报（中英文） 2024 , 5 (3) : 63 -74
基于智能化技术的果蔬农产品产地冷链物流发展现状与展望
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张睿智1 , 刘倩媛1, * , 黄雨婴1, 刘冰1, 常志光1, 王教领2, 3
作者信息
  • 1.中农国科（南京）规划设计有限公司，江苏 南京，210014
  • 2.农业农村部南京农业机械化研究所，江苏 南京，210014
  • 3.农业农村部农产品加工与贮藏重点实验室，江苏 南京，210014
通讯作者:
刘倩媛
Development status and prospect of cold chain logistics of fruits, vegetables, and agricultural products based on intelligent technology
Ruizhi ZHANG1 , Qianyuan LIU1, * , Yuying HUANG1, Bing LIU1, Zhiguang CHANG1, Jiaoling WANG2, 3
Affiliations
  • 1.Zhongnong Guoke （Nanjing） Planning and Design Co. Ltd., Nanjing 210014, China
  • 2.Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
  • 3.Key Laboratory of Agro-Products Processing and Storage, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
    • Corresponding Author:
    * LIU Qianyuan, engineer; research interests: agricultural engineering consulting and planning design. E-mail:

    ZHANG Ruizhi, engineer; research interests: agricultural engineering consulting and planning design. E-mail:

出版时间: 2024-08-15 doi: 10.12398/j.issn.2096-7217.2024.03.007
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果蔬产地冷链是降低产品损耗、提高产品品质的首要环节，更是薄弱环节。随着大数据技术在冷链物流中的应用，智能化设备与技术已成为提高果蔬产地冷链物流效率、延长果蔬保鲜期、降低果蔬损耗的重要载体，在果蔬预冷、分拣包装、检测、仓储、运输等方面有着诸多优势。本文通过研究总结国内外近几年发展迅速、使用广泛的智能化技术，包括以电磁或光学技术为基础的AGV智能搬运，以传感器、电子光学等技术为基础的智能分拣，以计算机视觉技术为基础的智能检测，以透视成像技术为基础的智能运输等，分析研究各学者在果蔬冷链仓储、分拣、检测、运输等环节中智能化技术应用的创新研究及取得的成果，提高了果蔬冷链物流效率。然而，智能化技术在果蔬冷链物流中的应用也面临诸多问题。成本高、技术融合难度高和人才短缺等挑战，使得智能化技术在果蔬冷链领域的发展受到一定限制。为解决当前存在的问题，提出通过自主研发、技术创新等手段进行成本控制，降低小型企业进入门槛。加强智能化技术与冷链物流系统的集成，提高数据安全性和系统兼容性。同时，政府应出台相关政策，提供必要的资金支持和人才培养机制。加快制定和完善冷链物流领域的智能化技术标准。通过技术创新、成本控制、人才培养和政策引导，推动农业产业升级，为果蔬冷链物流提质增效提供思路。

果蔬  /  冷链物流  /  AGV智能搬运  /  计算机视觉  /  光谱检测

The cold chain in the production area of fruits and vegetables is the primary link to reduce product loss and improve product quality, but it is also a weak link. With the application of big data technology in cold chain logistics, intelligent devices, and technologies have become important carriers for improving the efficiency of cold chain logistics in fruit and vegetable production areas, extending the shelf life of fruits and vegetables, and reducing fruit and vegetable losses. They have many advantages in fruit and vegetable pre-cooling, sorting and packaging, testing, warehousing, transportation, and other aspects. This article summarizes the rapidly developing and widely used intelligent technologies at home and abroad in recent years, including automated guided vehicle intelligent handling based on electromagnetic or optical technology, intelligent sorting based on sensors, electronic optics, and other technologies, intelligent detection based on computer vision technology, intelligent transportation based on perspective imaging technology, etc. It analyses and studies the innovative research and achievements of various scholars in applying intelligent technology in fruit and vegetable cold chain storage, sorting, detection, transportation, and other links, and improves the efficiency of fruit and vegetable cold chain logistics. However, applying intelligent technology in fruit and vegetable cold chain logistics also faces many problems. The challenges of high cost, difficulty in technological integration, and talent shortages have limited the development of intelligent technology in the field of fruit and vegetable cold chains. To solve the current problems, it is proposed that costs be controlled through independent research and development, technological innovation, and other means to lower the entry threshold for small enterprises. Strengthen integrating intelligent technology and cold chain logistics systems to improve data security and system compatibility. At the same time, the government should introduce relevant policies, provide necessary financial support, and establish talent training mechanisms. Accelerate the development and improvement of intelligent technology standards in the field of cold chain logistics. Through technological innovation, cost control, talent cultivation, and policy guidance, we aim to promote the upgrading of the agricultural industry and provide ideas for improving the quality and efficiency of fruit and vegetable cold chain logistics.

fruits and vegetables  /  cold chain logistics  /  automated guided vehicle intelligent handling  /  computer vision  /  spectral detection
张睿智, 刘倩媛, 黄雨婴, 刘冰, 常志光, 王教领. 基于智能化技术的果蔬农产品产地冷链物流发展现状与展望. 智能化农业装备学报（中英文）, 2024 , 5 (3) : 63 -74 . DOI: 10.12398/j.issn.2096-7217.2024.03.007
Ruizhi ZHANG, Qianyuan LIU, Yuying HUANG, Bing LIU, Zhiguang CHANG, Jiaoling WANG. Development status and prospect of cold chain logistics of fruits, vegetables, and agricultural products based on intelligent technology[J]. Journal of Intelligent Agricultural Mechanization, 2024 , 5 (3) : 63 -74 . DOI: 10.12398/j.issn.2096-7217.2024.03.007
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2024年第5卷第3期
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doi: 10.12398/j.issn.2096-7217.2024.03.007
  • 接收时间：2024-06-01
  • 首发时间：2026-11-05
  • 出版时间：2024-08-15
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  • 收稿日期：2024-06-01
  • 修回日期：2024-07-28
基金
National Natural Science Foundation of China(32301718)
Chinese Academy of Agricultural Sciences under the Special Institute-level Coordination Project for Basic Research Operating Costs(S202328)
作者信息
    1.中农国科（南京）规划设计有限公司，江苏 南京，210014
    2.农业农村部南京农业机械化研究所，江苏 南京，210014
    3.农业农村部农产品加工与贮藏重点实验室，江苏 南京，210014

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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