Abstract
Objective: In order to improve the precision of apple grade judgment model, the method of apple grade judgment was established. Methods: A decision model of apple rank based on multi-information fusion and dragonfly algorithm was proposed. Firstly, the HSV color feature, LBP texture feature and HOG shape feature of apple image were extracted by pre-processing such as data enhancement, normalization, Gauss filter and grayscale. Secondly, the performance of DBN model was affected by the selection of parameters, the network parameters of DBN model were optimized by DA algorithm, and a multi-information fusion and DA-DBN model for determining apple rank wws proposed. Results: Compared with GA-DBN, PSO-DBN, GWO-DBN and DBN, the model based on DA-DBN had the highest precision. Conclusion: The DBN model is optimized by dragonfly algorithm which can effectively improve the accuracy of apple rank determination model, which provides a new method for apple rank determination.
Publication Date
12-26-2023
First Page
138
Last Page
145
DOI
10.13652/j.spjx.1003.5788.2023.60055
Recommended Citation
Haixia, CHEN; Zhijuan, JIA; and Yunping, ZHAO
(2023)
"Apple classification based on multi-information fusion and DA-DBN,"
Food and Machinery: Vol. 39:
Iss.
10, Article 21.
DOI: 10.13652/j.spjx.1003.5788.2023.60055
Available at:
https://www.ifoodmm.cn/journal/vol39/iss10/21
References
[1] 孟庆龙, 尚静, 杨雪, 等. 基于BP网络的苹果硬度高光谱无损检测[J]. 包装工程, 2020, 41（15）: 14-18.
MENG Q L, SHANG J, YANG X, et al. Nondestructive detection for hyperspectral imaging of apple firmness based on BP network[J]. Packaging Engineering, 2020, 41（15）: 14-18.
[2] 赵杰文, 张海东, 刘木华. 利用近红外漫反射光谱技术进行苹果糖度无损检测的研究[J]. 农业工程学报, 2005, 21（3）: 162-165.
ZHAO J W, ZHANG H D, LIU M H. Non-destructive determination of sugar contents of apples using near infrared diffuse reflectance[J]. Transactions of the Chinese Society of Agricultural Engineering, 2005, 21（3）: 162-165.
[3] 刘云刚, 王伟. 基于SFLA优化的BP神经网络苹果鲜度气味识别系统[J]. 传感器与微系统, 2020, 39（8）: 96-99.
LIU Y G, WANG W. Apple fresh odor recognition system based on SFLA optimized BP neural network[J]. Transducer and Microsystem Technologies, 2020, 39（8）: 96-99.
[4] 夏阿林, 周新奇, 叶华俊, 等. 近红外光谱相似性评估结合局部回归方法无损检测苹果糖度[J]. 分析测试学报, 2010, 29（12）: 1 173-1 177.
XIA A L, ZHOU X Q, YE H J, et al. Non-destructive determination of sugar content in apple by near infrared spectroscopy with similarity evaluation combined with local regression method[J]. Journal of Instrumental Analysis, 2010, 29（12）: 1 173-1 177.
[5] MIRJALILI S. Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems[J]. Neural Computing & Applications, 2016, 27（4）: 1 053-1 073.
[6] ABDEL-ZAHER A M, ELDEIB A M. Breast cancer classification using deep belief networks[J]. Expert Systems with Applications, 2016（46）: 139-144.
[7] HUANG Y, TIAN K, WU A Q, et al. Feature fusion methods research based on deep belief networks for speech emotion recognition under noise condition[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10（5）: 1 787-1 798.
[8] 毛娟, 万鹏, 梁国平, 等. 基于区域适栽品种筛选的苹果品质综合评价模型的建立[J]. 食品工业科技, 2020, 41（19）: 60-66.
MAO J, WAN P, LIANG G P, et al. Establishment of comprehensive evaluation model of apple quality for regional suitable varieties selection[J]. Science and Technology of Food Industry, 2020, 41（19）: 60-66.
[9] 李速专, 童何馨, 袁雷明, 等. 间隔连续投影算法应用于近红外光谱苹果糖度模型的优化[J]. 食品安全质量检测学报, 2019, 10（14）: 4 608-4 612.
LI S Z, TONG H X, YUAN L M, et al. Optimization of near infrared spectroscopy model for sugar content in apple by intervals successive projection algorithm[J]. Journal of Food Safety & Quality, 2019, 10（14）: 4 608-4 612.
[10] 赵娟, 全朋坤, 张猛胜, 等. 基于特征LED光源的苹果多品质参数无损检测装置研究[J]. 农业机械学报, 2019, 50（4）: 326-332.
ZHAO J, QUAN P K, ZHANG M S, et al. Design of apple quality integrated non-destructive testing device based on multi-band LED light source[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50（4）: 326-332.
[11] ESWARAMOORTHY S, SIVAKUMARAN N, SEKARAN S. Grey wolf optimization based parameter selection for support vector machines[J]. Compel International Journal for Computation & Mathematics in Electrical & Electronic Engineering, 2016, 35（5）: 1 513-1 523.
[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] 刘燕德, 周延睿. 基于GA-LSSVM的苹果糖度近红外光谱检测[J]. 西北农林科技大学学报（自然科学版）, 2013, 41（7）: 229-234.
LIU Y D, ZHOU Y R. GA-LSSVM based near infrared spectroscopy detection of apple sugar content[J]. Journal of Northwest A & F University （Natural Science Edition）, 2013, 41（7）: 229-234.
[14] 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.