Abstract
［Objective ］ In order to improve the accuracy of egg appearance quality detection,an egg appearance quality detection model based on CNN -SVM model was established.［Methods ］ Combined with the adaptive feature extraction capability of CNN and the super -generalization classification capability of SVM,the features of fully connected layers were extracted by six -layer convolutional neural network structure processing,and the CNN -SVM hybrid model was adopted,instead of the traditional CNN + softmax,an egg appearance quality detection method based on CNN -SVM model was proposed.［Results］ Compared with SVM model,CNN model and KNN model,CNN -SVM model had better performance in accuracy,precision,recall and F1 score,which were 97.97%,98.10%,98.10% and 98.00% respectively.KNN model had the lowest accuracy in egg appearance quality detection,and its accuracy,precision,recall and F1 fraction are 77.46%,79.44%,76.75% and 76.90%,respectively.［Conclusion ］ The CNN -SVM model has strong robustness and anti -noise ability,which can effectively improve the accuracy and applicability of egg appearance quality detection..
Publication Date
2-18-2025
First Page
113
Last Page
119,156
DOI
10.13652/j.spjx.1003.5788.2024.60089
Recommended Citation
Ge, QI; Feng, ZHAO; and Wanning, LI
(2025)
"Egg appearance quality detection based on CNN-SVM model,"
Food and Machinery: Vol. 40:
Iss.
8, Article 16.
DOI: 10.13652/j.spjx.1003.5788.2024.60089
Available at:
https://www.ifoodmm.cn/journal/vol40/iss8/16
References
[1] 张晓芳,郁志宏,张涛,等.基于遗传神经网络的鸡蛋外观品质检测 [J].农机化研究,2008,30(5):122-124.ZHANG X F,YU Z H,ZHANG T,et al.Appearance quality of eggs detection based on genetic neural network [J].Journal of Agricultural Mechanization Research,2008,30(5):122-124.[2] 张樱飘,于亚萍,衡耿,等.基于图像处理鸡蛋表面污染物面积的检测条件比较研究 [J].机电工程技术,2020,49(10):72-74.ZHANG Y P,YU Y P,HENG G,et al.Research on detection condition comparison of egg contaminant based on image processing [J].Mechanical & Electrical Engineering Technology,2020,49(10):72-74.
[3] 文韬,代兴勇,李浪,等.基于机器视觉与光谱融合的柑橘品质无损检测分级系统设计与试验 [J].江苏大学学报 （自然科学版）,2024,45(1):38-45.WEN T,DAI X Y,LI L,et al.Design and experiment of non -destructive testing and grading system for citrus quality based on machine vision and spectral fusion [J].Journal of Jiangsu University (Natural Science Edition ),2024,45(1):38-45.
[4] DMITRY G,BO D,DUAA M,et al.Customized convolutional neural network for accurate detection of deep fake images in video collections [J].Computers,Materials & Continua,2024,79(5):1 995-2 014.
[5] WANNASIN D,GROSSMANN L,MCCLEMENTS D J.Optimizing the appearance of plant -based foods using natural pigments and color matching theory [J].Food Biophysics,2024,19(1):120-130.
[6] 李嘉宜,丁义文,艾民珉.光谱检测技术在禽蛋品质评价中的应 用 研 究 进 展 [J].食 品 安 全 质 量 检 测 学 报,2024,15(2):185-195.LI J Y,DING Y W,AI M M.Research progress on the application of spectroscopic detection technology in the quality evaluation of poultry eggs [J].Journal of Food Safety and Quality,2024,15(2):185-195.
[7] 庄珑昱,郑江霞.鸡蛋品质评价方法研究进展及其影响因素分析 [J].中国家禽,2024,46(3):87-94.ZHUANG L Y,ZHENG J X.Research progress on egg quality evaluation methods and their influencing factors [J].China Poultry,2024,46(3):87-94.
[8] MATSUGU M,MORI K,MITARI Y,et al.Subject independent facial expression recognition with robust face detection using a convolutional neural network [J].Neural Networks,2003,16(5):555-559.
[9] LIU W,DUAN C W,YU B,et al.Multi-pose pedestrian detection based on posterior HOG feature [J].Acta Electronica Sinica,2015,43(2):217-224.
[10] 赖嘉雯,汪宇玲,蔡晓宇,等.基于 CNN-SVM的多维度信息融合半月板撕裂分类方法 [J].波谱学杂志,2023,40(4):423-434.LAI J W,WANG Y L,CAI X Y,et al.Multidimensional information fusion method for meniscal tear classification based on CNN -SVM [J].Chinese Journal of Magnetic Resonance,2023,40(4):423-434.
[11] 李旭东,李艳军,曹愈远,等.基于 CNN-SVM的飞机 EHA故障 诊 断 算 法 研 究 [J].西 北 工 业 大 学 学 报,2023,41(1):230-240.LI X D,LI Y J,CAO Y Y,et al.Study on fault diagnosis algorithms of EHA based on CNN -SVM [J].Journal of Northwestern Polytechnical University,2023,41(1):230-240.
[12] 方明明,刘静.基于回归卷积神经网络的近红外光谱苹果脆片品质评价方法研究 [J].食品科技,2020,45(7):303-308.FANG M M,LIU J.Evaluation method of apple chips quality by near infrared spectroscopy based on regressive convolutional neural network [J].Food Science and Technology,2020,45(7):303-308.
[13] 王浩云,李晓凡,李亦白,等.基于高光谱图像和 3D-CNN的苹果多品质参数无损检测 [J].南京农业大学学报,2020,43(1):178-185.WANG H Y,LI X F,LI Y B,et al.Research on non -destructive detection of apple multi -quality parameters based on hyperspectral imaging technology and 3D-CNN [J].Journal of Nanjing Agricultural University,2020,43(1):178-185.
[14] LIU Y,ZENG L,HUANG Y.An efficient HOG -ALBP feature for pedestrian detection [J].Signal Image and Video Processing,2014,8(S1):125-134.
[15] VALAPPIL N K,MEMON Q A.CNN-SVM based vehicle detection for UAV platform [J].International Journal of Hybrid Intelligent Systems,2021,17(1):1-12.
[16] XUE D X,ZHANG R,FENG H,et al.CNN-SVM for microvascular morphological type recognition with data augmentation [J].Journal of Medical and Biological Engineering,2016,36(6):755-764.
