关键词: 湖北钉螺, 拟钉螺, 深度学习, 人工智能, 计算机视觉, 云南省

Abstract: Objective　To construct a visual intelligent recognition model for Oncomelania hupensis robertsoni in Yunnan Province based on the EfficientNet⁃B4 model, and to evaluate the impact of data augmentation methods and model hyperparameters on the recognition of O. hupensis robertsoni. Methods　A total of 400 O. hupensis robertsoni and 400 Tricula snails were collected from Yongsheng County, Yunnan Province in June 2024, and snail images were captured following identification and classification of 300 O. hupensis robertsoni and 300 Tricula snails. A total of 925 O. hupensis robertsoni images and 1 062 Tricula snail images were collected as a dataset and divided into a training set and validation set in a ratio of 8∶2, while 352 images captured from the other 100 O. hupensis robertsoni and 354 images from 100 Tricula snails served as an external test set. All acquired images were subjected to preprocessing, including cropping and resizing. Three data augmentation approaches were employed, including baseline, Mixup and Gaussian blurring, and model hyperparameters included two optimization algorithms of adaptive moment estimation (Adam) and stochastic gradient descent (SGD), two loss functions of focal loss and cross entropy loss, and two learning rate decay strategies of cosine annealing and multi⁃step. The intelligent recognition models of O. hupensis robertsoni and Tricula snails were constructed based on the EfficientNet⁃B4 model, and 7 training strategy groups were generated by combinations of different data augmentation approaches and hyperparameters. The performance of intelligent recognition models was tested with external test sets, and evaluated with accuracy, precision, recall, F1 score, loss, Youden's index, and the area under the receiver operating characteristic curve (AUC) under different training strategies. Results　The variation of loss values was comparable among intelligent recognition models with different data augmentation approaches. The Group 4 model constructed with Mixup and Gaussian blurring data augmentation approaches showed the optimal performance, with an accuracy of 90.38%, precision of 90.07%, F1 score of 89.44%, Youden's index of 0.81 and AUC values of 0.961 in the external test set. The accuracy of models using the SGD optimizer reduced by 29.16% as compared to those using the Adam optimizer ([χ2] = 81.325, P < 0.001), and the accuracy of models using the cross entropy loss function reduced by 0.80% as compared to the Group 4 model ([χ2] = 3.147, P > 0.05), while the accuracy of models using the multi⁃step learning rate decay strategy increased by 0.65% as compared to the Group 4 model ([χ2] = 0.208, P > 0.05). In addition, the model with the baseline + Mixup + Gaussian blurring data augmentation approach and hyperparameters of Adam optimizer, focal loss function and multi⁃step learning rate decay strategy showed the highest performance, with an accuracy of 91.03%, precision of 91.97%, recall of 88.11%, F1 score of 90.00%, Youden's index of 0.82 and AUC values of 0.969 in external test set, respectively. Conclusions　The intelligent recognition model of O. hupensis robertsoni based on EfficientNet⁃B4 model is accurate for identification of O. hupensis robertsoni and Tricula snails in Yunnan Province.

Key words: Oncomelania hupensis, Tricula, Deep learning, Artificial intelligence, Computer vision, Yunnan Province