基于 MobileNetV3-large 模型的葡萄品种识别.

Grape(Vitis vinifera L.) has wide varieties and different characteristics, and traditional cultivar identification methods have disadvantages such as slow recognition speed, low accuracy, strong subjectivity, high recognition cost, strong subjectivity, and poor timeliness. Therefore, development of the grape variety recognition technologies with fast recognition speed, high accuracy, low cost, and strong timeliness have important theoretical significance and practical value. To provide a theoretical basis for precision agriculture to realize the non-destructive and efficient identification of grape varieties, in this study, based on the leaf morphological characteristics, five fresh grape varieties including Zaohaibao, Wuhezaohong, Xiahei, Hongdiqiu, and Yangguangmeigui were used as materials, and the transfer learning network model MobileNet-large were used to analyze the effects of transfer learning of the model on five grape varieties, training results of three kinds of MobileNet-large network models were compared, and a MobileNetV3-large grape variety recognition model based on leaf images was constructed. The results showed that transfer learning before training could significantly improve the recognition rate of grape varieties, and the correct recognition rate of Wuhezaohong could reach 100%. The accuracy, recall, F1 score, and AUC of MobileNetV3-large network were different due to different grape varieties and learning rates. The network training loss of MobileNetV3-large model had the smallest at the learning rate of 0.005, and the accuracy of Hongdiqiu was the highest. Comparing three kinds of MobileNet-large network models, the MobileNetV3-large model performed the best overall, converging from the 27th round of training, with a Top-1 accuracy of 90.56%, an average accuracy of 97.50%, indicating that the MobileNetV3-large model was an appropriate identification network model for grape varieties. [ABSTRACT FROM AUTHOR]