Construction of apricot variety search engine based on deep learning

文献类型： 外文期刊

第一作者： Chen, Chen

作者： Chen, Chen;Wang, Lin;Liu, Huimin;Xu, Wanyu;Huang, Mengzhen;Gou, Ningning;Wang, Chu;Bai, Haikun;Wuyun, Tana;Chen, Chen;Liu, Jing;Jia, Gengjie;Chen, Chen;Wang, Lin;Liu, Huimin;Xu, Wanyu;Huang, Mengzhen;Gou, Ningning;Wang, Chu;Bai, Haikun;Wuyun, Tana;Chen, Chen;Wang, Lin;Liu, Huimin;Xu, Wanyu;Huang, Mengzhen;Gou, Ningning;Wang, Chu;Bai, Haikun;Wuyun, Tana

作者机构：

关键词： Apricot; Variety; Convolutional neural network; Deep learning; Database platform; Mobile application; Image retrieval

期刊名称：HORTICULTURAL PLANT JOURNAL （ 影响因子：5.7； 五年影响因子：5.4 ）

ISSN： 2095-9885

年卷期： 2024 年 10 卷 2 期

页码：

收录情况： SCI

摘要： Apricot has a long history of cultivation and has many varieties and types. The traditional variety identification methods are timeconsuming and labor-consuming, posing grand challenges to apricot resource management. Tool development in this regard will help researchers quickly identify variety information. This study photographed apricot fruits outdoors and indoors and constructed a dataset that can precisely classify the fruits using a U-net model (F-score: 99%), which helps to obtain the fruit's size, shape, and color features. Meanwhile, a variety search engine was constructed, which can search and identify variety from the database according to the above features. Besides, a mobile and web application (ApricotView) was developed, and the construction mode can be also applied to other varieties of fruit trees. Additionally, we have collected four difficult-to-identify seed datasets and used the VGG16 model for training, with an accuracy of 97%, which provided an important basis for ApricotView. To address the difficulties in data collection bottlenecking apricot phenomics research, we developed the first apricot database platform of its kind (ApricotDIAP, http://apricotdiap.com/) to accumulate, manage, and publicize scientific data of apricot.

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