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

Abstract

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.

Plant phenotyping relevance

植物葉の病徴を画像から検出・分類するCNN手法の開発と性能評価が研究の中心であり、植物の病害状態を推定するフェノタイピング手法に該当する。

Code and data availability

The paper uses two public plant leaf image datasets directly in its analysis: the Plant Village dataset (Kaggle) as the primary training/classification dataset and the RoCoLe dataset (datasetninja) for coffee leaf disease samples. Both have explicit public URLs in the references. No author code, models, or checkpoints,