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Development of a mobile application for identification of grapevine (Vitis vinifera L.) cultivars via deep learning

Liu, Yixue and Su, Jinya and Shen, Lei and Lu, Nan and Fang, Yulin and Liu, Fei and Song, Yuyang and Su, Baofeng (2021) 'Development of a mobile application for identification of grapevine (Vitis vinifera L.) cultivars via deep learning.' International Journal of Agricultural and Biological Engineering, 14 (5). pp. 172-179. ISSN 1934-6344

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Traditional vine variety identification methods usually rely on the sampling of vine leaves followed by physical, physiological, biochemical and molecular measurement, which are destructive, time-consuming, labor-intensive and require experienced grape phenotype analysts. To mitigate these problems, this study aimed to develop an application (App) running on Android client to identify the wine grape automatically and in real-time, which can help the growers to quickly obtain the variety information. Experimental results showed that all Convolutional Neural Network (CNN) classification algorithms could achieve an accuracy of over 94% for twenty-one categories on validation data, which proves the feasibility of using transfer deep learning to identify grape species in field environments. In particular, the classification model with the highest average accuracy was GoogLeNet (99.91%) with a learning rate of 0.001, mini-batch size of 32 and maximum number of epochs in 80. Testing results of the App on Android devices also confirmed these results.

Item Type: Article
Uncontrolled Keywords: deep learning; mobile phone; grapevine cultivar; vine leaf image; identification; Vitis vinifera L
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 03 Nov 2021 12:40
Last Modified: 15 Jan 2022 01:38

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