| 基于改进CNN的草莓成熟度分类方法 |
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| 引用本文： | 张效禹,黄国言,杨永涛,包 锋.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. |
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| 作者姓名： | 张效禹 黄国言 杨永涛 包 锋 |
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| 作者单位： | 1. 河北对外经贸职业学院;2. 燕山大学;3. 东北石油大学 |
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| 基金项目： | 国家自然科学基金(编号：62276225)； |
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| 摘 要： | 目的:提高草莓分类准确率。方法:通过混合池化方法对CNN进行改进，提出基于改进CNN的草莓分类方法。通过最大池化和平均池化技术组合，得到混合池化方法；通过混合池化方法对CNN进行改进，提高CNN模型的泛化能力；进行图像数据采集、图像预处理和提取图像特征；并利用灵敏度、特异度、精确度、召回率和F1分数对训练好的草莓分类方法进行分类效果评估。结果:试验方法对16像素×16像素图像中草莓分类的灵敏度、特异度、精确度、召回率和F1分数分别达到0.993,0.993,0.994,0.992,0.991;与其他5种分类方法相比，试验方法对草莓分类的灵敏度、特异度、精确度、召回率和F1分数分别平均提高了3.44%,3.96%,4.26%,3.92%,4.08%。结论:该方法可实现不同成熟度草莓的准确分类。
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| 关 键 词： | 卷积神经网络 混合池化 草莓 成熟度 分类 |
| 收稿时间： | 2023/3/16 0:00:00 |
A method for strawberry ripeness classification method based on improved CNN |
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| ZHANG Xiaoyu,HUANG Guoyan,YANG Yongtao,BAO Feng.A method for strawberry ripeness classification method based on improved CNN[J].Food and Machinery,2023,39(10):130-137. |
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| Authors: | ZHANG Xiaoyu HUANG Guoyan YANG Yongtao BAO Feng |
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| Affiliation: | Hebei Institute of International Business and Economics, Qinhuangdao, Hebei 066311, China;Yanshan University, Qinhuangdao, Hebei 066044, China; Northeast Petroleum University, Qinhuangdao, Hebei 066000, China |
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| Abstract: | Objective: To improve the classification accuracy of strawberries. Methods: A method of strawberry classification based on improved CNN was proposed by improving CNN through mixing pool method. Firstly, through the combination of maximum pooling and average pooling techniques, a hybrid pooling method was obtained. Then, the hybrid pool method was used to improve the generalization ability of CNN model. After that, image data acquisition, image preprocessing and image feature extraction were carried out. Finally, sensitivity, specificity, accuracy, recall rate and F1 score were used to evaluate the effectiveness of the trained strawberry classification method. Results: The sensitivity, specificity, accuracy, recall rate and F1 score of the proposed method for strawberry classification in 16 pixel×16 pixel images reached 0.993, 0.993, 0.994, 0.992 and 0.991, respectively. Compared with the other five classification methods, the sensitivity, specificity, accuracy, recall rate and F1 score of the proposed method were improved by 3.44%, 3.96%, 4.26%, 3.92% and 4.08%, respectively. Conclusion: This method can achieve accurate classification of strawberries with different maturity, and is expected to provide technical support for the research and development of high-performance strawberry packaging robots and supermarket fruit automatic recognition machines. |
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| Keywords: | convolutional neural network mixing pool strawberries maturity classification |
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