RESEARCH ON CLASSIFICATION METHODS OF BARLEY PLANTS BASED ON TRANSFER LEARNING

Classification of barley plants plays a crucial role in understanding barley varietal diversity and breeding. Traditional classification methods rely on expert experience and require significant manual effort. With the rise of deep learning based on machine vision technologies, particularly the emergence of transfer learning, the issue of model overfitting on small datasets has been mitigated, leading to enhanced generalization capabilities. This study employs a self-constructed barley plant image dataset to compare five state-of-the-art deep learning models, while analyzing the impacts of various factors - including image resolution and training-test split ratio - on classification accuracy. The results indicate that the DenseNet model achieves the best classification performance at an input resolution of 512×512 pixels, with an accuracy of 96.02%. Increasing the proportion of training data further improved performance, with the 80%:20% training-test split ratio yielding optimal results across all five models. Transfer learning models outperform training from scratch, with EfficientNet-v2 achieving the highest accuracy of 98.86%. Additionally, gradient-weighted class activation mapping (Grad-CAM) was utilized to generate heatmap visualizations of the decision-making processes in each transfer learning model. By applying deep learning for barley plant classification and selecting the optimal model, this research provides a reliable technical solution for barley variety identification and classification.