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熊棣文,孔文斌,冯洋.在树柑橘果实识别与定位技术发展现状及展望[J].中国南方果树,2021,50(2):
在树柑橘果实识别与定位技术发展现状及展望
The Development Status and Future Prospection of On-Tree Citrus Fruit recognition and Locating Technology
投稿时间:2021-02-04  修订日期:2021-03-08
DOI:
中文关键词:  无人采摘  深度学习  点云数据  目标识别与定位  农机装备智能化
英文关键词:automatic harvesting  deep learning  point cloud data  object detection and locating  intelligent agricultural machinery
基金项目:重庆市技术创新与应用示范(产业类)重点研发项目 (cstc2018jszx-cyzdX0041);重庆市现代山地特色高效农业技术体系创新团队建设计划(特色水果产业技术体系)2020-03
作者单位E-mail
熊棣文* 中国科学院重庆绿色智能技术研究院 dirkpitt@126.com 
孔文斌 重庆市农业技术推广总站 476945377@qq.com 
冯洋 重庆市农业技术推广总站 171521718@qq.com 
摘要点击次数: 1829
全文下载次数: 2348
中文摘要:
      柑橘是我国三大水果之一,近年来,随着农村劳动力紧缺加剧,柑橘果实采摘成本大幅增加。为缓解这一矛盾,国家加大了对农机装备智能化的重视与支持,智能农机装备研发进入了快速发展期,一些智能化技术已在相关农业产业实现了应用。但是,在柑橘果实无人采摘方面,技术尚未成熟,也缺乏实际应用,关键瓶颈是在树柑橘果实的快速精准识别与定位。本文探讨了目前国内外在本领域的研究进展及其在实际应用中的优势与缺陷,具体分析了存在的瓶颈难点,介绍了在本研究领域颇具潜力的基于点云数据的目标识别与定位技术,总结了其在柑橘果实识别与定位中可能面临的挑战,并对此领域的后续研究进行了展望。
英文摘要:
      Citrus is one of the three main fruit industry in China, recently, the cost of citrus fruit harvesting is increasing vastly due to the intensify of the lack of labor resource in rural area. To ease this situation, China has increased its support to the intellectual upgrade of the traditional agricultural machinery and equipment, hence the intelligent agricultural machinery industry has developed rapidly, and some of the machines have been utilized in industry production. However, in citrus fruit automatic harvesting, there is little applicable research results and lack of actual utilization. The bottleneck problem in this area is how to rapidly recognize and locate the on-tree citrus fruits. Based on this notion, this paper discussed the research progress and results in this area over the world along with their cons and pros in actual utilization. Moreover, this paper introduced a new data format named point cloud, and the object recognition techniques based on point cloud, which has great potential in on-tree citrus fruit recognition and locating area. This paper also discussed about the challenges and possible solutions in applying point cloud data to citrus fruit recognition and locating.
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