基于GA-SVM的农产品质量分类

doi:10.11896/JsJkx.190900184

计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 517-520.doi: 10.11896/JsJkx.190900184

• 数据库 & 大数据 & 数据科学 • 上一篇下一篇

基于GA-SVM的农产品质量分类

马创¹, 吕孝飞², 梁炎明²

1 重庆邮电大学软件学院重庆 400065;
2 重庆邮电大学计算机科学与技术学院重庆 400065

发布日期:2020-07-07
通讯作者: 吕孝飞(424085240@qq.com)
作者简介:machuang@cqupt.edu.cn
基金资助:
国家自然科学基金面上项目(6172099);重庆市“三百”科技创新领军人才支持计划(CSTCCXLJRC201917);重庆市人工智能技术创新重大主题专项(CSTC2017rgzn-zdyf0140);重庆市技术创新与应用示范重大主题专项项目(CSTC2018JSZX-CYZTZX0178,CSTC2018JSZX-CYZTZX0185)

Agricultural Product Quality Classification Based on GA-SVM

MA Chuang¹, LV Xiao-fei² and LIANG yan-ming²

1 College of Software Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
2 College of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China

Published:2020-07-07
About author:MA Chuang, born in 1984, Ph.D, asso-ciate professor, is a member of China Computer Federation.His main research interests include complex network, and machine learning.
LV Xiao-fei, born in 1995, postgradua-te, is a member of China Computer Fe-deration.His main research interests include machine learning.
Supported by:
This work was supported by the National Natural Science Foundation of China (6172099),Chongqing “300” Science and Technology Innovation Leader Talents Support Plan (CSTCCXLJRC201917),Chongqing Artificial Intelligence Technology Innovation MaJor Theme ProJect (CSTC2017rgzn-zdyf0140) and Chongqing Technical Innovation and Application Demonstration MaJor Theme ProJect (CSTC 2018JSZX-CYZTZX0178,CSTC 2018JSZX-CYZTZX0185).

摘要/Abstract

摘要： 传统方法对农产品进行细粒度划分,确定影响分类效果的关键因素,但忽略了农产品的质量特征。对农产品的质量进行科学的分类,能够更好地反映农产品在质量方面的变化,还可以显著提升农产品后续的处理效率。从农产品的质量特征出发,将农产品进行分类,对不同类别的农产品按照不同的方法进行处理,以在保证农产品质量的同时提高农产品的附加值。分类方法与模型参数的选取对于农产品质量分类的准确度尤为重要。传统支持向量机SVM对模型参数的选择具有盲目性,为提高分类的准确度,文中提出一和中将因子分析(Factor Analsysi,FA)与基于遗传算法改进的支持向量机(Genetic Algorithm-Support Vector Machine,GA-SVM)结合的分类模型。实验结果表明,改进后的SVM能够快速、有效地判别农产品质量类别,显著改善农产品质量的分类精度,评估过程较为简单,可广泛应用于农产品质量的评估。

关键词: 农产品, 遗传算法, 因子分析, 支持向量机, 质量分类

Abstract: Traditional methods classify agricultural products by fined-grained level and determine the key factors affecting the classification effect,but ignore the quality characteristics of agricultural products.Scientific classification of agricultural products quality can not only effectively improve the speed of subsequent processing of agricultural products,but also better reflect the changes in the quality of agricultural products.Starting from the quality characteristics of agricultural products,agricultural pro-ducts are classified,and different types of agricultural products are processed in different methods,so as to ensure the quality of agricultural products and increase their added values.The classification method and the selection of model parameters are especially important for the accuracy of agricultural product quality classification.Traditional support vector machine (SVM) has blindness in the selection of model parameters.In order to improve the classification accuracy of agricultural product quality,a product quality classification model combining factor analysis (FA) and improved support vector machine (GA-SVM) is proposed.Experimental results show that the improved SVM can quickly and effectively identify the quality categories of agricultural products,significantly improve the classification accuracy of agricultural product quality.The evaluation process is relatively simple and can be widely applied to the evaluation of agricultural product quality.

Key words: Agricultural products, Factor analysis, Genetic algorithm, Quality classification, SVM

中图分类号:

TP391

马创, 吕孝飞, 梁炎明. 基于GA-SVM的农产品质量分类[J]. 计算机科学, 2020, 47(6A): 517-520. https://doi.org/10.11896/JsJkx.190900184

MA Chuang, LV Xiao-fei and LIANG yan-ming. Agricultural Product Quality Classification Based on GA-SVM[J]. Computer Science, 2020, 47(6A): 517-520. https://doi.org/10.11896/JsJkx.190900184

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基于GA-SVM的农产品质量分类

Agricultural Product Quality Classification Based on GA-SVM

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