Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (20): 24-33.DOI: 10.3778/j.issn.1002-8331.1907-0089

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Research Status of Deep Learning in Agriculture of China

LV Shengping, LI Denghui, XIAN Rongheng   

  1. College of Engineering, South China Agricultural University, Guangzhou 510642, China
  • Online:2019-10-15 Published:2019-10-14



  1. 华南农业大学 工程学院,广州 510642

Abstract: Deep Learning(DL) has been widely used in intelligent agriculture for plant disease detection, plant and fruit recognition, crop and weed detection and classification and so on. 65 articles from 2014 to 2019 on the application of DL in agriculture of China are presented. At first, the basic concept and development history of DL are briefly introduced, and the article retrieval and distribution of the reviewed articles are given. Subsequently, the articles are reviewed from various points of view such as research object, data source, inter-class differences, preprocessing, data-augmentation, framework and performance comparison. Eventually, the advantages and disadvantages of DL are analyzed, and its development trends in agriculture are demonstrated.

Key words: deep learning, intelligent agriculture, detection, recognition, classification, prediction

摘要: 深度学习(Deep Learning,DL)已广泛应用于智能农业的病虫害检测、植物和水果识别、农作物及杂草检测与分类等研究中。对2014年至2019年国内发表的65篇有关DL在农业中应用研究成果进行综述。简要介绍DL的基本概念及其发展历史,给出了所选论文检索方法及其分布;对所选论文从研究对象与目的、数据来源、类间差异、预处理、数据扩增、模型框架以及性能对比等角度进行了综述;对DL的优缺点进行了分析,并指明了其在智能农业研究中的发展趋势。

关键词: 深度学习, 智能农业, 检测, 识别, 分类, 预测