@article{Chen2024, 
author = {Chen Chen and Lin Wang and Huimin Liu and Jing Liu and Wanyu Xu and Mengzhen Huang and Ningning Gou and Chu Wang and Haikun Bai and Gengjie Jia and Tana Wuyun},
title = {Construction of apricot variety search engine based on deep learning},
year = {2024},
journal = {Horticultural Plant Journal},
volume = {10},
number = {2},
pages = {387-397},
keywords = {Apricot, Variety, Convolutional neural network, Deep learning, Database platform, Mobile application, Image retrieval},
url = {https://www.sciopen.com/article/10.1016/j.hpj.2023.02.007},
doi = {10.1016/j.hpj.2023.02.007},
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.}
}