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mIT-CMCA: a cross-modal category alignment framework for robust maize disease identification.

Feilong Tang^1,2, Rosalyn R Porle³, Hoe Tung Yew²

¹College of Mechanical and Electrical Engineering, Xichang University, Xichang, 615013, China.

Scientific Reports

|April 21, 2026

Summary

A new maize disease identification framework, maize image-text Cross-Modal Category Alignment (mIT-CMCA), improves accuracy and efficiency. This approach reduces annotation complexity and computational overhead for practical agricultural applications.

Keywords:

Attention mechanism Category-level textual descriptions Cross-modal alignment Maize disease identification Multimodal learning

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Accurate maize disease identification is vital for global food security.
Traditional methods face challenges with lighting, occlusions, and noise.
Existing multimodal approaches require extensive manual annotation, limiting scalability.

Purpose of the Study:

To develop a robust and efficient maize disease identification framework.
To reduce the annotation burden in multimodal learning.
To enhance model performance under complex conditions.

Main Methods:

Proposed a maize image-text framework with Cross-Modal Category Alignment (mIT-CMCA).
Implemented category-level alignment between image and text modalities in a shared embedding space.
Introduced a Cross-Modal Category Alignment (CMCA) loss inspired by contrastive learning.
Integrated an Efficient Channel-Spatial Hybrid Attention (CSHA) module for improved feature discriminability.

Main Results:

mIT-CMCA achieved 99.48% accuracy on the maize subset of the PlantVillage dataset (MPVD).
On the Maize Leaf-Field dataset (MLFD), the model attained 93.67% accuracy.
The model uses 71.6% fewer parameters and is 72.3% smaller than MaxViT_tiny.
Demonstrated superior robustness against artificially added perturbations.

Conclusions:

mIT-CMCA offers a favorable balance between accuracy and efficiency for maize disease identification.
The framework effectively addresses limitations of traditional and existing multimodal methods.
The proposed approach is suitable for practical deployment in agriculture.