Ensemble Deep Learning Models for Grapevine Leaf Image Classification

The classification of grapevine leaf diseases is crucial for effective vineyard management, as it aids in early detection and treatment of diseases, ultimately improving yield and quality. This paper presents a deep learning-based approach using ensemble models for the classification of grapevine leaf images. By combining multiple deep learning models, the proposed method leverages the strengths of each individual model to improve accuracy, robustness, and generalization. A dataset consisting of images of grapevine leaves is used to evaluate the effectiveness of the ensemble approach. The results show that the ensemble model outperforms individual models in terms of classification accuracy and robustness to varying environmental conditions. Keywords · Grapevine leaf classification · Deep learning · Ensemble models · Image classification · Plant disease detection · Convolutional Neural Networks (CNN) · Data augmentation · Transfer learning