Introduction Reliable identification of plant diseases from leaf images is essential for effective crop monitoring and the prevention of yield deterioration. With the growing adoption of deep learning in agricultural applications, convolutional neural network–based classifiers have demonstrated notable success in visual plant disease recognition. Methods In this study, we propose PlantPathNet , a purpose-built deep learning architecture for plant disease classification. Input images are transformed from the RGB to the HSV color space to enhance the representation of disease-related visual features. A novel Cross-layer Feature Integration Module (CFIM) is introduced to effectively aggregate discriminative features across multiple network depths. Additionally, an efficient channel attention mechanism based on ECANet is incorporated to emphasize disease-relevant representations. The model is optimized using a composite loss function combining modified softmax loss and center loss to address class imbalance and improve feature separability. Results Extensive experiments conducted on the PlantVillage dataset demonstrate that PlantPathNet outperforms several state-of-the-art models, including ResNet-50, Inception-V3, DenseNet121, VGG16, and Vision Transformer-based approaches. The proposed model achieves an overall accuracy of 99.57%, precision of 99.52%, recall of 99.54%, F1-score of 99.53%, and an AUROC of 99.84%. Discussion The results indicate that the integration of HSV-based preprocessing, CFIM, and channel attention significantly enhances classification performance. The proposed framework provides a robust and efficient solution for automated plant disease diagnosis and has strong potential for real-world agricultural applications.
Sönmez et al. (Wed,) studied this question.