重庆理工大学学报2025，Vol.39Issue(17)：133-141,9.DOI:10.3969/j.issn.1674-8425(z).2025.09.017
基于TomatoVit的番茄病害分类与分级研究
Research on tomato disease detection based on tomatovit
摘要
Abstract
To address the challenges of identifying diseases in tomato cultivation,such as detection costs,strong subjectivity and poor timeliness,this paper proposes a tomato disease classification and grading method based on Vision Transformer.A novel model,TomatoVit,is introduced,which consists of three core modules:(1)a hybrid backbone network based on ResNet-50 and Vision Transformer for extracting disease features;(2)a Feature Self-Validation(FSV)module that enhances the model's ability to recognize subtle variations in disease features through token replacement and prediction mechanisms;and(3)a Multi-Scale Global-Local Attention(MSGL)module,which combines global and local attention mechanisms to better capture the fine-grained features of disease variations.This approach transforms tomato disease detection from simple classification to severity evaluation,subdividing eight common tomato diseases into three stages:healthy,early,and severe stage.It enables monitoring of disease progression.Experiments conducted on a tomato disease dataset comprising 18 categories demonstrate the proposed model achieves an accuracy of 89.79％and an F1-score of 0.90,outperforming other methods.关键词
图像分类/视觉Transformer/番茄病害分类/病害分级Key words
image classification/vision transformer/tomato disease classification/disease severity grading分类
信息技术与安全科学引用本文复制引用
孔祥源,王一群,缪祎晟,陈雯柏,赵春江..基于TomatoVit的番茄病害分类与分级研究[J].重庆理工大学学报,2025,39(17):133-141,9.基金项目
国家科技创新2030—"新一代人工智能"重大项目(2021ZD0113603) （2021ZD0113603）
