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Position: Home > Articles > Construction of apricot variety search engine based on deep learning Horticultural Plant Journal 2024,10 (2)

Construction of apricot variety search engine based on deep learning

作  者：
Chen Chen;Lin Wang;Huimin Liu;Jing Liu;Wei Xu;Mengzhen Huang;Ningning Gou;Chu Wang;Haikun Bai;Gengjie Jia;Tana Wuyu
单  位：
State Key Laboratory of Tree Genetics and Breeding, Research Institute of Non-timber Forestry, Chinese Academy of Forestry, Zhengzhou, Henan 450003, China; Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China;Key Laboratory of Non-timber Forest Germplasm Enhancement and Utilization of National Forestry and Grassland Administration, Zhengzhou, Henan 450003, China; State Key Laboratory of Tree Genetics and Breeding, Research Institute of Non-timber Forestry, Chinese Academy of Forestry, Zhengzhou, Henan 450003, Chin;Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China
关键词：
variety;apricot;identify;developed;the fruit;search engin
摘  要：
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.

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