Plant leaf disease severity identification using hybrid fusion of texture and transformer-based features

The effective management of biotic stresses contributes to the agricultural resilience and sustainability. In this respect, the proposed system introduces a hybrid fusion of textural features with deep features derived from a Vision Transformer to enhance the characterisation of the disease severity. Texture features are extracted from the grey level co-occurrence matrix, which is computed on leaf images having undergone a prior Gabor filtering. The classification stage is achieved by machine learning techniques including a multi-layer perceptron and a support vector machine. Additionally, we propose an algorithm to assign labels of severity levels for unlabelled datasets. This algorithm utilises a U-Net architecture to extract masks of diseased regions, where the ratio with the total leaf area allows the severity estimation. Experiments were conducted on datasets including images of pear leaves and wheat spikes labelled into multiple severity levels as well as unlabelled tomato leaves affected by late blight. The proposed system outperforms various deep learning models by more than 3% on the overall classification accuracy and seems to be well-suited for multi-species disease severity classification tasks. Also, it offers an accuracy of 94.2%, 91.8% and 91.11% on the tomato, pear, and wheat spike datasets, exceeding all state-of-the-art results.