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Construction of apricot variety search engine based on deep learning
- Source :
- Horticultural Plant Journal, Vol 10, Iss 2, Pp 387-397 (2024)
- Publication Year :
- 2024
- Publisher :
- KeAi Communications Co., Ltd., 2024.
-
Abstract
- Apricot has a long history of cultivation and has many varieties and types. The traditional variety identification methods are time-consuming 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.
Details
- Language :
- English
- ISSN :
- 24680141
- Volume :
- 10
- Issue :
- 2
- Database :
- Directory of Open Access Journals
- Journal :
- Horticultural Plant Journal
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.2df3a37c15c2444a90735cd2837cb5eb
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.hpj.2023.02.007