Multi-FusNet-convolutional neural network with improved Huber loss function for plant leaf disease detection and classification.

Background: Recently, plant disease detection and classification have become major concerns in agriculture. Early detection of plant diseases supports farmers to take precautionary actions to prevent the spread of infections across different parts of the plant. However, detecting and classifying plant leaf diseases remain challenging tasks due to the overlapping characteristics of different diseases. Methods: To mitigate these limitations, this research developed a Multi-FusNet-convolutional neural network (Multi-FusNet-CNN) with an improved Huber loss function to classify multiple classes of plant leaf diseases. Here, a multipath residual network (Multi-RG) with cross-filtering fusion is integrated, and the pixel shuffling fusion method is developed for fusing low-level to up-sampled features. An improved Huber loss function is incorporated into the Multi-FusNet-CNN to effectively handle outliers and enhance the model's generalization capability during training. Results: The developed Multi-FusNet-CNN with improved Huber loss function achieved 99.95% accuracy, 99.13% F1-score, 99.87% recall, 99.27% precision, and 99.93% specificity, thereby outperforming existing conventional techniques. Conclusion: The proposed Multi-FusNet-CNN model improved the generalization capability of the method during the training process on plant leaf disease detection and classification.