湖北农业科学 ›› 2018, Vol. 57 ›› Issue (24): 112-115.doi: 10.14088/j.cnki.issn0439-8114.2018.24.031

• 园艺·特产 • 上一篇    下一篇

基于BP神经网络的柚子分类研究

黄杰贤, 刘燕, 杨冬涛   

  1. 嘉应学院电子信息工程学院,广东 梅州 514015
  • 收稿日期:2018-09-30 出版日期:2018-12-25 发布日期:2020-04-01
  • 作者简介:黄杰贤(1982-),男,广东梅州人,讲师,主要从事人工智能研究,(电话)13430140734(电子信箱)huangjiexian@126.com。
  • 基金资助:
    2014梅州市产业技术研究开发资金项目(201415); 2014广东省扬帆计划博士后扶持项目

The Classification of Grapefruit Based on BP Neural Network

HUANG Jie-xian, LIU Yan, YANG Dong-tao   

  1. School of Electronic Information Engineering,Jiaying University,Meizhou 514015,Guangdong,China
  • Received:2018-09-30 Online:2018-12-25 Published:2020-04-01

摘要: 介绍了一种基于BP神经网络的柚子分类检测系统。通过选取柚子表面缺陷、柚子形状以及大小等指标构建BP神经网络模型,从而得到较好的网络权值。然后选取20组数据进行验证。结果表明,基于BP神经网络能够准确地对柚子进行模拟分类,准确率达90%。

关键词: BP神经网络, 柚子分类, 柚子特征

Abstract: A grapefruit classification detection system based on BP neural network was introduced. BP neural network model was built by selecting the grapefruit surface defects, grapefruit shape, grapefruit size and other indicators, in order to get better network weights. Then 20 sets of data for verification were selected. The experimental results indicated that grapefruit classification could be accurately classified by using BP neural network with the accuracy of 90%.

Key words: BP neural network, grapefruit classification, grapefruit characteristics

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