Plant Disease Classification Using Hybrid Features

Deep learning has shown remarkable performances in image classification, including those of plants and leaves. However, high-performing networks in terms of accuracy may not be using the salient regions for making the prediction and could be prone to biases. This work proposes a neural network architecture incorporating handcrafted features and fusing them with the learned features. Using hybrid features provides better control and understanding of the feature space while leveraging deep learning capabilities. Furthermore, a new IoU-based metric is introduced to assess the CNN-based classifier’s performance based on the regions focused on making predictions. Experiments over multiple leaf disease datasets demonstrate the performance improvement with the model using hybrid features. Classification using hybrid features performed better than the baseline models in terms of P@1 and also on the IoU-based metric.