基于改进鲸鱼优化CNN的红富士苹果外观分级方法  

A red Fuji apple appearance grading method based on improved whale optimization algorithm and CNN

作　　者：刘素娇 卢明星[1] 王春芳[2] 赵梓枫 刘怡 LIU Sujiao;LU Mingxing;WANG Chunfang;ZHAO Zifeng;LIU Yi(Henan Vocational College of Nursing,Anyang,Henan 455000,China;Henan University of Science and Technology,Luoyang,Henan 471000,China;Henan Agricultural University,Zhengzhou,Henan 450002,China)

机构地区：[1]河南护理职业学院,河南安阳455000 [2]河南科技大学,河南洛阳471000 [3]河南农业大学,河南郑州450002

出　　处：《食品与机械》2024年第4期121-126,共6页Food and Machinery

基　　金：河南省教育厅高等学校青年骨干教师培养计划资助项目(编号:2016GGJS-285)。

摘　　要：目的:有效提升机器视觉技术对红富士苹果外观品质分级的准确率。方法:建立不同外观品质等级的红富士苹果图像数据库,通过对数据库图像进行图像增强预处理,以提高模型训练效果和泛化能力。构造改进鲸鱼优化CNN模型,采用加权灰色关联度法压缩CNN卷积规模,以降低特征间的冗余度干扰和提高模型的运算速度;利用改进的鲸鱼优化算法对模型超参数进行优化配置,以降低超参数配置不当对模型分级结果的影响。结果:试验所提分级方法准确率更高,分级精确度、灵敏度分别提高了2.05%,2.46%。结论:试验方法能够有效实现对红富士苹果的外观分级。Objective:In order to improve the accuracy of machine vision technology in grading the appearance quality of red Fuji apples,a red Fuji apple appearance grading method based on improved whale optimization algorithm(WOA)and CNN is proposed.Methods:A red Fuji apple image database with different appearance quality levels was established,and the database images were preprocessed so as to improve the training effect and generalization ability of the model.The improved CNN-LSTM was designed as the weighted grey correlation method was used to compress the CNN convolution scale,in order to reduce redundant interference between features and improve the computational speed of the model.The improved whale optimization algorithm was used to optimize the hyperparameters configuration of CNN-LSTM,effectively reducing the impact of improper hyperparameter configuration on model classification results.Results:The simulation results showed that the proposed classification method had a higher accuracy,with classification accuracy and sensitivity improved by about 2.05%and 2.46%.Conclusion:The proposed method can effectively achieve the appearance grading of red Fuji apples.

关 键 词：苹果 分级 深度学习 机器视觉 准确率 

分 类 号：S226.5[农业科学—农业机械化工程] TP391.41[农业科学—农业工程] TP18[自动化与计算机技术—计算机应用技术]