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

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.

Automated summary

This study developed an Android client application (App) for automatic and real-time identification of wine grape varieties, achieving over 94% accuracy using Convolutional Neural Network (CNN) classification algorithms. The GoogLeNet model performed the best with an accuracy of 99.91%, demonstrating the feasibility of using transfer deep learning to identify grape species in field environments.