# A precision grading method for walnut leaf brown spot disease integrating hierarchical feature selection and dynamic multi-scale convolution

## TL;DR

This paper introduces a new model for accurately grading walnut leaf brown spot disease using advanced image processing techniques.

## Contribution

The study proposes a novel model, CogFuse-MobileViT, integrating hierarchical feature selection and dynamic multi-scale convolution for disease grading.

## Key findings

- The proposed model achieves 86.61% accuracy on the test set.
- It outperforms existing models by 7.8 percentage points.
- The model effectively addresses blurred lesion edges and complex feature extraction.

## Abstract

Walnut leaf brown spot disease, caused by Ophiognomonia leptostyla, is among the most destructive fungal diseases in walnut cultivation. In the development of smart agriculture, precision grading of plant diseases remains a core technical challenge; specifically, this disease is plagued by blurred lesion edges and inefficient extraction of complex features, which directly limits the accurate grading of the disease. To address these issues, this study proposes a disease grading method integrating hierarchical feature selection and adaptive multi-scale dilated convolution, and develops the CogFuse-MobileViT model. This model overcomes the limitations of the standard MobileViTv3 model in capturing blurred edges of tiny lesions via three innovative modules: specifically, the Hierarchical Feature Screening Module (HFSM) enables hierarchical screening of disease-related features; the Edge Feature Focus Module (ECFM) works in synergy with the HFSM to enhance the focus on lesion edge features; and the Adaptive Multi-Scale Dilated Convolution Fusion Module (AMSDIDCM) achieves dynamic multi-scale fusion of lesion textures and global structures. Experimental results demonstrate that the proposed model achieves an accuracy of 86.61% on the test set, representing an improvement of 7.8 percentage points compared with the original MobileViTv3 model and significantly outperforming other mainstream disease grading models. This study confirms that the CogFuse-MobileViT model can effectively resolve the issues of blurred edges and inefficient feature extraction in this disease, provides a reliable technical solution for its precision grading, and holds practical application value for the intelligent diagnosis of plant diseases in smart agriculture.