원문정보
Lesion-Guided Four-Channel ConvNeXt for Tomato Disease Recognitio
초록
영어
This study presents a lesion-guided four-channel ConvNeXt model for tomato plant disease recognition. By segmenting lesion areas in the HSV color space, grayscale masks are generated and combined with RGB images to form a four-channel input. The proposed ConvNeXt4Channel network, optimized for this input, enhances spatial feature extraction. Experiments on the PlantVillage tomato dataset (train-test split: 8:2) show that the model, trained with cross-entropy loss and Adam optimizer (learning rate = 1e-4), achieves 96.81% accuracy—surpassing conventional models by approximately 2.5%. Grad-CAM visualizations indicate improved lesion localization, confirming the effectiveness of lesion-guided enhancement. This method provides a robust and interpretable solution for automated crop disease diagnosis.
목차
1. Introduction
2. Related Work
3. Methodologies
3.1 Disease Spot Region Extraction
3.2 Construction of Four-Channel Input
3.3 Training and Optimization Strategy
4. Experiments
4.1 Experimental Settings
4.2 Overall Performance
4.3 Comparison with Other Models
4.4 Grad-CAM Visualization Analysis
5. Conclusion
Acknowledgement
References