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

Yixue Liu, Jinya Su, Lei Shen, Nan Lu, Yulin Fang, Fei Liu, Yuyang Song, Baofeng Su*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
6 Downloads (Pure)


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) miming 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.

Original languageEnglish
Pages (from-to)172-179
Number of pages8
JournalInternational journal of agricultural and biological engineering
Issue number5
Publication statusPublished - 1 Sept 2021

Bibliographical note

Acknowledgements: The authors would like to express their gratitude to the Teaching Experiment Farm of Ningxia University, for their kind help. This study was supported by the Key R & D projects of Ningxia Hui Autonomous Region (Grant No. 2019BBF02013)


  • deep learning
  • mobile phone
  • grapevine cultivar
  • vine leaf image
  • identification
  • Vitis vinifera L.


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