Journal of Agricultural Science and Technology ›› 2022, Vol. 24 ›› Issue (10): 79-89.DOI: 10.13304/j.nykjdb.2021.0688

• INTELLIGENT AGRICULTURE & AGRICULTURAL MACHINERY • Previous Articles     Next Articles

Application and Research Advances on Deep Learning in Apple’s Industry Chain

Hao HUANG1(), Shengqiao XIE1, Du CHEN2(), Heng WANG3   

  1. 1.College of Engineering,China Agricultural University,Beijing 100083,China
    2.Beijing Key Laboratory for Optimal Design of Modern Agricultural Equipment,China Agricultural University,Beijing 100083,China
    3.Luoyang Intelligent Agricultural Equipment Research Institute Co. ,Ltd. ,Henan Luoyang 471934,China
  • Received:2021-08-12 Accepted:2021-11-22 Online:2022-10-15 Published:2022-10-25
  • Contact: Du CHEN

深度学习在苹果产业链中的应用与研究进展

黄昊1(), 谢圣桥1, 陈度2(), 王恒3   

  1. 1.中国农业大学工学院,北京 100083
    2.中国农业大学现代农业装备优化设计北京市重点实验室,北京 100083
    3.洛阳智能农业装备研究院有限公司,河南 洛阳 471934
  • 通讯作者: 陈度
  • 作者简介:黄昊E-mail:cauhuanghao@163.com
  • 基金资助:
    烟台市校地融合发展项目(2021XDRHXMPT29)

Abstract:

China is a big apple producer, with wide apple planting areas and many varieties. Combining deep learning with machine vision technology and applying it to the entire industrial chain of apple planting and production are important means and direction for the technological upgrading of the apple industry. This article focused on 3 key stages of fruit tree planting, harvesting and postharvest inspection in the apple industry chain, and systematically combed the related application and research progress of deep learning technology, which mainly included leaf disease and insect pest identification, planting monitoring, target detection of harvesting robot and non-destructive grading testing of apple after harvest. Based on the analysis and comparison of the technical differences and commonalities between different technologies, the difficulties and challenges that deep learning faced in apple industry chain were discussed.

Key words: deep learning, apple, machine vision, convolutional neural network

摘要:

我国是苹果生产大国,苹果种植面积广、品种多。将深度学习与机器视觉技术相结合并运用于苹果种植和生产的全产业链中是苹果产业技术升级的重要手段和方向。聚焦苹果产业链中的果树种植、收获采摘和产后检测3个关键阶段,系统性梳理深度学习技术的相关应用与研究进展,其中主要涉及叶部病虫害识别、种植监测、采摘机器人的目标识别和苹果无损分级检测等研究领域,在分析对比不同技术之间的差异与共性的基础上,探讨深度学习在苹果产业链中所面临的困难与挑战。

关键词: 深度学习, 苹果, 机器视觉, 卷积神经网络

CLC Number: