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An explainable hybrid feature aggregation network with residual inception positional encoding attention and
M Sundara Srivathsan1, S Alden Jenish1, K Arvindhan1
1School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Scientific Reports
|April 6, 2025
Summary
This study introduces a new AI model for classifying cassava leaf diseases, achieving 93.06% accuracy. The novel dual-track architecture improves disease identification, aiding farmers and enhancing crop management.
Area of Science:
- Agricultural Science
- Computer Science
- Plant Pathology
Background:
- Cassava leaf diseases significantly impact crop yields, farmer livelihoods, and market stability.
- Traditional manual disease diagnosis is inefficient, costly, and labor-intensive.
- Existing automated methods struggle with the complexity and variability of cassava leaf disease symptoms.
Purpose of the Study:
- To develop an accurate and efficient automated system for classifying cassava leaf diseases.
- To overcome the limitations of current methods in handling complex and variable disease symptoms.
- To introduce a novel dual-track feature aggregation architecture for improved disease classification.
Main Methods:
- Proposed a novel dual-track feature aggregation architecture integrating the Residual Inception Positional Encoding Attention (RIPEA) Network with EfficientNet.
- The RIPEA track utilized residual connections and multi-scale feature fusion with Coordinate and Mixed Attention mechanisms.
- Employed image augmentation and a cosine decay learning rate schedule for model training.
- Applied Grad-CAM for model interpretability and visual explanation of classification decisions.
Main Results:
- The proposed model achieved a classification accuracy of 93.06% in differentiating between Cassava Bacterial Blight (CBB), Brown Streak Disease (CBSD), Green Mottle (CGM), Mosaic Disease (CMD), and healthy cassava leaves.
- The dual-track architecture effectively integrated local textures and global structures for accurate classification.
- Grad-CAM provided visual insights into the model's decision-making process, highlighting critical leaf regions.
Conclusions:
- The novel dual-track feature aggregation architecture demonstrates high efficacy in classifying cassava leaf diseases.
- This AI-driven approach offers a promising solution for accurate and efficient disease diagnosis, supporting sustainable agriculture.
- The model's interpretability enhances trust and understanding in automated disease management systems.
