外文摘要：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.
外文关键词：deep learning;Convolutional Neural Network;variety;mobile application;Apricot;Database platform;Image retrieval
作者：Wang, Lin;Liu, Huimin;Chen, Chen;Liu, Jing;Xu, Wanyu;Huang, Mengzhen;Gou, Ningning;Wang, Chu;Bai, Haikun;Jia, Gengjie;Wuyun, Tana
作者单位：Chinese Acad Agr Sci;Natl Forestry & Grassland Adm;Res Inst Nontimber Forestry;State Forestry & Grassland Adm
期刊名称：HORTICULTURAL PLANT JOURNAL
期刊影响因子：0.0
出版年份：2024
出版刊次：10（2）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台