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- Title
Construction of apricot variety search engine based on deep learning.
- Authors
Chen Chen; Lin Wang; Huimin Liu; Jing Liu; Wanyu Xu; Mengzhen Huang; Ningning Gou; Chu Wang; Haikun Bai; Gengjie Jia; Tana Wuyun
- Abstract
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.
- Subjects
APRICOT; SEARCH engines; DEEP learning; PLANT phenomorphology; CONVOLUTIONAL neural networks
- Publication
Horticulture Plant Journal, 2024, Vol 10, Issue 2, p387
- ISSN
2095-9885
- Publication type
Article
- DOI
10.1016/j.hpj.2023.02.007