Abstract:

[Objective] Desert vegetation is an indispensable part of desert ecosystems, and its conservation and restoration are crucial. Accurate identification of desert plants is an indispensable task, and is the basis of desert ecological research and conservation. The complex growth environment caused by light, soil, shadow and other vegetation increases the recognition difficulty, and the generalization ability is poor and the recognition accuracy is not guaranteed. The rapid development of modern technology provides new opportunities for plant identification and classification. By using intelligent identification algorithms, field investigators can be effectively assisted in desert plant identification and classification, thus improve efficiency and accuracy, while reduce the associated human and material costs. [Methods] In this research, the following works were carried out for the recognition of desert plant: Firstly, a training dataset of deep learning model of desert plant images in the arid and semi-arid region of Xinjiang was constructed to provide data resources and basic support for the classification and recognition of desert plant images.The desert plant image data was collected in Changji and Tacheng region from the end of September 2021 and July to August 2022, and named DPlants50. The dataset contains 50 plant species in 13 families and 43 genera with a total of 12,507 images, and the number of images for each plant ranges from 183 to 339. Secondly, a migration integration learning-based algorithm for desert plant image recognition was proposed, which could effectively improve the recognition accuracy. Taking the EfficientNet B0-B4 network as the base network, the ImageNet dataset was pre-trained by migration learning, and then an integrated learning strategy was adopted combining Bagging and Stacking, which was divided into two layers. The first layer introduced K-fold cross-validation to divide the dataset and trained K sub-models by borrowing the Stacking method. Considering that the output features of each model were the same in this study, the second layer used Bagging to integrate the output features of the first layer model by voting method, and the difference was that the same sub-models and K sub-models were compared to select the better model, so as to build the integrated model, reduce the model bias and variance, and improve the recognition performance of the model. For 50 types of desert plants, 20% of the data was divided as the test set, and the remaining 5 fold cross validation was used to divide the dataset, then can use DPi(i=1,2,…,5) represents each training or validation set. Based on the pre trained EfficientNet B0-B4 network, training and validation were conducted on 5 data subsets. Finally, the model was integrated using soft voting, hard voting, and weighted voting methods, and tested on the test set. [Results and Discussions] The results showed that the Top-1 accuracy of the single sub-model based on EfficientNet B0 network was 92.26%~93.35%, the accuracy of the Ensemble-Soft model with soft voting, the Ensemble-Hard model with hard voting and the Ensemble-Weight model integrated by weighted voting method were 93.63%, 93.55% and 93.67%, F₁ Score and accuracy were comparable, the accuracy and F₁ Score of Ensemble-Weight model integrated by weighted voting method were not significantly improved compared with Ensemble-Soft model and Ensemble-hard model, but it showed that the effect of weighted voting method proposed in this study was better than both of them. The three integrated models demonstrate no noteworthy enhancements in accuracy and F₁ Score when juxtaposed with the five sub-models. This observation results suggests that the homogeneity among the models constrains the effectiveness of the voting method strategy. Moreover, the recognition effects heavily hinges on the performance of the EfficientNet B0-DP5 model. Therefore, the inclusion of networks with more pronounced differences was considered as sub-models. A single sub-model based on EfficientNet B0-B4 network had the highest Top-1 accuracy of 96.65% and F₁ Score of 96.71%, while Ensemble-Soft model, Ensemble-Hard model and Ensemble-Weight model got the accuracy of 99.07%, 98.91% and 99.23%, which further improved the accuracy compared to the single sub-model, and the F₁ Score was basically the same as the accuracy rate, and the model performance was significant. The model integrated by the weighted voting method also improved accuracy and F₁ Score for both soft and hard voting, with significant model performance and better recognition, again indicating that the weighted voting method was more effective than the other two. Validated on the publicly available dataset Oxford Flowers102, the three integrated models improved the accuracy and F₁ Score of the three sub-models compared to the five sub-models by a maximum of 4.56% and 5.05%, and a minimum of 1.94% and 2.29%, which proved that the migration and integration learning strategy proposed in this paper could effectively improve the model performances.