Leaf Identification Based on Convolutional Neural Networks and Data Augmentation

Plant leaves, as one of the significant morphological characteristics of plants, exhibit considerable interspecific variability, making them highly important in plant classification and identification. Traditional manual leaf recognition methods rely heavily on expert experience, which presents challenges such as low efficiency and strong subjectivity. Although image classification methods based on convolutional neural networks (CNNs) have been widely applied to plant leaf recognition in recent years, their classification performance is still constrained by the scale of training data and the generalization capability of models. To address these limitations, this paper proposes an automated leaf recognition model that integrates convolutional neural networks with data augmentation techniques. The proposed approach incorporates multiple data augmentation strategies during the preprocessing stage to enhance data diversity and constructs a deep convolutional network architecture specifically tailored for leaf images. Experiments were conducted on the Flavia dataset, and the results demonstrate that the proposed model achieves a classification accuracy of 99.74% on the test set, significantly surpassing the baseline model's accuracy of 93.72%. These findings validate the effectiveness and superiority of the proposed method in the task of plant leaf recognition.