HSMK-YOLO: Improved YOLOv11 fused with hypergraph convolutional network for tomato leaf disease detection

2026-04-21

PRE

Abudukelimu Abulizi

Junxiang Ye

Halidanmu Abudukelimu *

Gengrong Zhang

Cuiqin Guo

Yajun Zhang

Dengfeng Yao

DOI：10.1016/j.asoc.2026.115286

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Abstract

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• Hypergraph with MaNet captures semantic-structural patterns to improve disease detection. • HSMK-YOLO reduces parameters by 12.3% and GFLOPs by 0.6%, achieving 92.1% P, and 94.7% mAP50:95. • KAN and SCConv reconstruct the backbone and C3k2 module to enhance feature extraction. • MultiSEAM head reduces computational cost and improves detection accuracy.

Keywords:

Hypergraph Convolutional Network

YOLOv11

Tomato Leaf Disease Detection

Feature Extraction

MultiSEAM Head

HSMK-YOLO: Improved YOLOv11 fused with hypergraph convolutional network for tomato leaf disease detection

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