Abstract
［Objective］ To improve the accuracy and efficiency of the automatic grading method for yellow peaches on the food production line ［Methods］ Based on the yellow peach automatic grading system (machine vision and hyperspectral technology ),a new method for automatic detection of yellow peach quality is proposed,which integrates an improved YOLOv 11 and an improved extreme learning machine (ELM ).External quality images are captured by a CMOS sensor camera,and defects are identified using the improved YOLOv 11 model.The external quality is determined by the fruit shape index and color.Internal quality is detected using a hyperspectral instrument,and after feature selection,the data is input into an ELM model optimized by an improved grey wolf algorithm to detect soluble solids and hardness as internal quality indicators.The yellow peach is graded based on both external and internal qualities.The performance of the method is verified through experiments.［Results］ The experimental method effectively detects both the internal and external qualities of yellow peaches on the food production line,with a high grading accuracy and efficiency,achieving a grading accuracy greater than 95.00% and an average grading time of less than 0.3 seconds.［Conclusion］ By combining machine vision,hyperspectral technology,and intelligent algorithms,rapid and non -destructive detection of food quality can be achieved.
Publication Date
6-13-2025
First Page
89
Last Page
97
DOI
10.13652/j.spjx.1003.5788.2025.60003
Recommended Citation
Yongjie, PENG; Liangjun, ZHAO; and Xuming, LONG
(2025)
"Automatic grading method of yellow peaches on food production line based on improved YOLOv 11 and GWO-ELM,"
Food and Machinery: Vol. 41:
Iss.
5, Article 13.
DOI: 10.13652/j.spjx.1003.5788.2025.60003
Available at:
https://www.ifoodmm.cn/journal/vol41/iss5/13
References
[1] 邹金萍,章帅,董文韬,等.应用高光谱图像检测鱼肉挥发性盐基总氮含量研究 [J].光谱学与光谱分析,2021,41(8):2 586-2 590.ZOU J P,ZHANG S,DONG W T,et al.Application of hyperspectral image to detect the content of total nitrogen in fish meat volatile base [J].Spectroscopy and Spectral Analysis,2021,41(8):2 586-2 590.
[2] 丛军,李星.基于电子鼻、电子舌技术的荣昌猪肉及其制品贮藏过程新鲜度检测研究 [J].食品安全质量检测学报,2024,15(7):192-201.CONG J,LI X.Detection of freshness of Rongchang pork and its products during storage based on electronic nose and electronic tongue technology [J].Journal of Food Safety & Quality,2024,15(7):192-201.
[3] 刘美辰,薛河儒,刘江平,等.牛奶蛋白质含量的 SSA-SVM 高光 谱 预 测 模 型 [J].光 谱 学 与 光 谱 分 析,2022,42(5):1 601-1 606.LIU M C,XUE H R,LIU J P,et al.Hyperspectral analysis of milk protein content using SVM optimized by sparrow search algorithm [J].Spectroscopy and Spectral Analysis,2022,42(5):1 601-1 606.
[4] ERNA K H,ROVINA K,MANTIHAL S.Current detection techniques for monitoring the freshness of meat-based products:a review [J].Journal of Packaging Technology and Research,2021,5(3):127-141.
[5] 王俊平,徐刚.机器视觉和电子鼻融合的番茄成熟度检测方法[J].食品与机械,2022,38(2):148-152.WANG J P,XU G.Research on tomato maturity detection method based on machine vision and electronic nose fusion [J].Food & Machinery,2022,38(2):148-152.
[6] 陈 伟,张 春 雨,朱 超 冉.基 于 YOLOv 5s算 法 的 番 茄 成 熟 度 识别研究 [J].安徽科技学院学报,2023,37(1):92-95.CHEN W,ZHANG C Y,ZHU C R.Research on tomato maturity recognition based on YOLOv 5s algorithm [J].Journal of Anhui of Science and Technology University,2023,37(1):92-95.
[7] 张 凡,淑 英,张 志 胜,等.融 合 光 谱 和 图 像 特 征 信 息 的 羊 肉 TVB-N 含 量 无 损 检 测 [J].中 国 食 品 学 报,2021,21(11):191-200.ZHANG F,SHU Y,ZHANG Z S,et al.Nondestructive detection of TVB-N content in mutton based on fused spectra and image information [J].Journal of Chinese Institute of Food Science and Technology,2021,21(11):191-200.
[8] 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.
[9] 孙潇鹏,刘灿灿,陆华忠,等.基于近红外透射光谱与机器视觉的蜜柚汁胞粒化分级检测 [J].食品科学技术学报,2021,39(1):37-45.SUN X P,LIU C C,LU H Z,et al.Detection of honey pomelo in different granulation levels based on near-infrared transmittance spectroscopy combined with machine vision [J].|Journal of Food Science and Technology,2021,39(1):37-45.
[10] 郭德超,饶远立,张豪,等.结合机器视觉和光谱技术的番茄综合品质检测方法 [J].食品与机械,2024,40(9):123-130.GUO D C,RAO Y L,ZHANG H,et al.Comprehensive quality detection method for tomatoes combining machine vision and spectral techniques [J].Food & Machinery,2024,40(9):123-130.
[11] 靳学萌,梁西银,邓鹏飞.基于改进 YOLOv 10的轻量级黄花菜分级检测模型 [J].智慧农业 (中英文 ),2024,6(5):108-118.JIN X M,LIANG X Y,DENG P F.Lightweight daylily grading and detection model based on improved YO-LOv 10[J].Smart Agriculture,2024,6(5):108-118.
[12] LI B,YIN H,LIU Y D,et al.Detection storage time of mild bruise's yellow peaches using the combined hyperspectral imaging and machine learning method [J].Journal of Analytical Science and Technology,2022,13(1):1-12.
[13] 许程翔,赵明岩,梁喜凤,等.基于改进 YOLOv 8的轻量化甘薯 品 质 分 级 实 验 研 究 [J].实 验 技 术 与 管 理,2024,41(6):47-56.XU C X,ZHAO M Y,LIANG X F,et al.Experimental study on lightweight sweet potato quality grading based on improved YOLOv 8[J].Experimental Technology and Management,2024,41(6):47-56.
[14] 谢安国,纪思媛,李月玲,等.基于遗传算法和深度神经网络的近红外高光谱检测猪肉新鲜度 [J].食品工业科技,2024,45(17):345-351.XIE A G,JI S Y,LI Y L,et al.Detection of pork freshness using NIR hyperspectral imaging based on genetic algorithm and deep neural network [J].Science and Technology of Food Industry,2024,45(17):345-351.
[15] 牛超,杨卫东,胡鹏明,等.Wi-freshness:基于 CSI 的猪肉新鲜度检测系统研究 [J].物联网学报,2023,7(2):143-152.NIU C,YANG W D,HU P M,et al.Wi freshness:research on CSI-based pork freshness detection system [J].Chinese Journal on Internet of Things,2023,7(2):143-152.
[16] 刘小花,周彬静,彭菁,等.基于电子鼻和高光谱成像技术的冷 鲜 牛 肉 微 生 物 的 生 长 模 型 构 建 [J].南 京 农 业 大 学 学 报,2023,46(3):595-605.LIU X H,ZHOU B J,PENG J,et al.Modeling of microbial growth in chilled beef based on the E-nose and hyperspectral imaging techniques [J].Journal of Nanjing Agricultural University,2023,46(3):595-605.
[17] 胡鹏伟,刘江平,薛河儒,等.BP神经网络结合变量选择方法在牛奶蛋白质含量检测中的应用 [J].光电子·激光,2022,33(1):23-29.HU P W,LIU J P,XUE H R,et al.Application of BP neural network and variable selection method in protein content detection of milkt [J].Journal of Optoelectronics·Laser,2022,33(1):23-29.
[18] 李玉花,史翰卿,熊赟葳,等.融合电子鼻和视觉技术的鸡肉新 鲜 度 检 测 装 置 研 究 [J].农 业 机 械 学 报,2022,53(11):433-440.LI Y H,SHI H Q,XIONG Y W,et al.Research of chicken freshness detection device based on electronic nose and vision technology [J].Journal of Agricultural Machinery,2022,53(11):433-440.
[19] 周雨帆,李胜旺,杨奎河,等.基于轻量级卷积神经网络的苹果 表 面 缺 陷 检 测 方 法 [J].河 北 工 业 科 技,2021,38(5):388-394.ZHOU Y F,LI S W,YANG K H,et al.Apple surface defect detection method based on lightweight convolutional neural network [J].Hebei Journal of Industrial Science and Technology,2021,38(5):388-394.
[20] 李艳坤,董汝南,张进,等.光谱数据解析中的变量筛选方法[J].光谱学与光谱分析,2021,41(11):3 331-3 338.LI Y K,DONG R N,ZHANG J,et al.Variable selection methods in spectral data analysis [J].Spectroscopy and Spectral Analysis,2021,41(11):3 331-3 338.