Multiclass cucumber leaf diseases recognition using best feature selection

Agriculture is an important research area in the field of visual recognition by computers. Plant diseases affect the quality and yields of agriculture. Early-stage identification of crop disease decreases financial losses and positively impacts crop quality. The manual identification of crop diseases, which are mostly visible on leaves, is a very time-consuming and costly process. In this work, we propose a new framework for the recognition of cucumber leaf diseases. The proposed framework is based on deep learning and involves the fusion and selection of the best features. In the feature extraction phase, VGG (Visual Geometry Group) and Inception V3 deep learning models are considered and fine-tuned. Both fine-tuned models are trained using deep transfer learning. Features are extracted in the later step and fused using a parallel maximum fusion approach. In the later step, best features are selected using Whale Optimization algorithm. The best-selected features are classified using supervised learning algorithms for the final classification process. The experimental process was conducted on a privately collected dataset that consists of five types of cucumber disease and achieved accuracy of 96.5%. A comparison with recent techniques shows the significance of the proposed method.