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...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.展开更多
基金supported by the Key R&D projects of Ningxia Hui Autonomous Region(Grant No.2019BBF02013).
文摘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.
