Abstract
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.
Publication Date
5-21-2024
First Page
121
Last Page
126
DOI
10.13652/j.spjx.1003.5788.2023.60150
Recommended Citation
Sujiao, LIU; Mingxing, LU; Chunfang, WANG; Zifeng, ZHAO; and Yi, LIU
(2024)
"A red Fuji apple appearance grading method based on improved whale optimization algorithm and CNN,"
Food and Machinery: Vol. 40:
Iss.
4, Article 19.
DOI: 10.13652/j.spjx.1003.5788.2023.60150
Available at:
https://www.ifoodmm.cn/journal/vol40/iss4/19
References
[1] 何进荣, 石延新, 刘斌, 等. 基于DXNet模型的富士苹果外部品质分级方法研究[J]. 农业机械学报, 2021, 52（7）: 379-385.
HE J R, SHI Y X, LIU B, et al. Externalquality grading method of Fuji apple based on deep learning[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52（7）: 379-385.
[2] 刘佳浩, 高军伟, 张炳星, 等. 基于机器视觉的水果分级系统[J]. 食品与机械, 2023, 39（6）: 112-118.
LIU J H, GAO J W, ZHANG B X, et al. Design of fruit grading system based on machine vision[J]. Food & Machinery, 2023, 39（6）: 112-118.
[3] 张效禹, 黄国言, 杨永涛, 等. 基于改进CNN的草莓成熟度分类方法[J]. 食品与机械, 2023, 39（10）: 130-137.
ZHANG X Y, HUANG G Y, YANG Y T, et al. A method for strawberry ripeness classification method based on improved CNN[J]. Food & Machinery, 2023, 39（10）: 130-137.
[4] 吕金锐, 付燕, 倪美玉, 等. 基于改进YOLO v4模型的番茄成熟度检测方法[J]. 食品与机械, 2023, 39（9）: 134-139.
LU J R, FU Y, NI M Y, et al. Research on tomato maturity detection method based on improved YOLOv4 model[J]. Food & Machinery, 2023, 39（9）: 134-139.
[5] 张立杰, 周舒骅, 李娜, 等. 基于改进SSD卷积神经网络的苹果定位与分级方法[J]. 农业机械学报, 2023, 54（6）: 223-232.
ZHANG L J, ZHOU S H, LI N, et al. Applelocation and classification based on improved SSD convolutional neural network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54（6）: 223-232.
[6] 张灵枝, 黄艳, 于英杰, 等. 基于近红外光谱技术的六大茶类快速识别[J]. 食品与生物技术学报, 2024, 43（1）: 48-59.
ZHANG L Z, HUANG Y, YU Y J, et al. Rapid identification of six major tea categories based on near-infrared spectroscopy[J]. Journal of Food Science and Biotechnology, 2024, 43（1）: 48-59.
[7] CHAO X F, SUN G Y, ZHAO H K, et al. Identification of apple tree leaf diseases based on deep learning models[J]. Symmetry, 2020, 12（7）: 1 065.
[8] 薛勇, 王立扬, 张瑜, 等. 基于GoogLeNet深度迁移学习的苹果缺陷检测方法[J]. 农业机械学报, 2020, 51（7）: 30-35.
XUE Y, WANG L Y, ZHANG Y, et al. Defect detection method of apples based on GoogLeNet deep transfer learning[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51（7）: 30-35.
[9] MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.
[10] 何文斌, 魏爱云, 明五一, 等. 基于机器视觉的水果品质检测综述[J]. 计算机工程与应用, 2020, 56（11）: 10-16.
HE W B, WEI A Y, MING W Y, et al. Survey of fruit quality detection based on machine vision[J]. Computer Engineering and Applications, 2020, 56（11）: 10-16.
[11] 张忠志, 薛欢庆, 范广玲. 基于改进卷积神经网络的红枣缺陷识别[J]. 食品与机械, 2021, 37（8）: 158-162, 192.
ZHANG Z Z, XUE H Q, FAN G L. Research on jujube defect recognition method based on improved convolution neural network[J]. Food & Machinery, 2021, 37（8）: 158-162, 192.
[12] 武东辉, 许静, 陈继斌, 等. 基于融合注意力机制与CNN-LSTM 的人体行为识别算法[J]. 科学技术与工程, 2023, 23（2）: 681-689.
WU D H, XU J, CHEN J B, et al. Human activity recognition algorithm based on CNN-LSTM with attention mechanism[J]. Science Technology and Engineering, 2023, 23（2）: 681-689.
[13] 杨保军, 梁琪, 宋雪梅. 基于计算机虚拟技术研究牦牛乳硬质干酪苦味肽的抑菌活性差异[J]. 食品与生物技术学报, 2021, 40（12）: 75-87.
YANG B J, LIANG Q, SONG X M. Study on difference of antibacterial activity of bitter peptide from yak milk hard cheese based on computer virtual technology[J]. Journal of Food Science and Biotechnology, 2021, 40（12）: 75-87.
[14] LI M D, XU G H, FU Y W, et al. Improved whale optimization algorithm based on variable spiral position update strategy and adaptive inertia weight[J]. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2022, 42（3）: 1 501-1 517.
[15] 汤安迪, 韩统, 徐登武, 等. 混沌多精英鲸鱼优化算法[J]. 北京航空航天大学学报, 2021, 47（7）: 1 481-1 494.
TANG A D, HAN T, XU D W, et al. Chaotic multi-leader whale optimization algorithm[J]. Jounal of Beijing University Aeronautics and Astronautics, 2021, 47（7）: 1 481-1 494.
[16] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344（6 191）: 1 492-1 496.
[17] 陈叶旺, 申莲莲, 钟才明, 等. 密度峰值聚类算法综述[J]. 计算机研究与发展, 2020, 57（2）: 378-394.
CHEN Y W, SHEN L L, ZHONG C M, et al. Survey on density peak clustering algorithm[J]. Journal of Computer Research and Development, 2020, 57（2）: 378-394.
[18] 王晓形. 基于AHP及专家打分法的大跨度隧道风险评估[J]. 现代隧道技术, 2020, 57（S1）: 233-240.
WANG X X. Risk assessment of the large-span tunnel based on AHP and expert scoring method[J]. Modern Tunnelling Technology, 2020, 57（S1）: 233-240.
[19] 万薇, 卜莹雪, 王祥, 等. 基于改进ResNet模型的食品新鲜度识别方法[J]. 食品与机械, 2023, 39（9）: 123-127.
WAN W, BU Y X, WANG X, et al. Food freshness recognition method based on improved ResNet model[J]. Food & Machinery, 2023, 39（9）: 123-127.