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LT-YOLO: A Lightweight Network for Detecting Tomato Leaf Diseases

Zhenyang He, Mengjun Tong*

College of Mathematics and Computer Science, Zhejiang Agricultural and Forestry University, Hangzhou, 310000, China

* Corresponding Author: Mengjun Tong. Email: email

Computers, Materials & Continua 2025, 82(3), 4301-4317. https://doi.org/10.32604/cmc.2025.060550

Abstract

Tomato plant diseases often first manifest on the leaves, making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry. However, conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery. This paper proposes a lightweight model for detecting tomato leaf diseases, named LT-YOLO, based on the YOLOv8n architecture. First, we enhance the C2f module into a RepViT Block (RVB) with decoupled token and channel mixers to reduce the cost of feature extraction. Next, we incorporate a novel Efficient Multi-Scale Attention (EMA) mechanism in the deeper layers of the backbone to improve detection of critical disease features. Additionally, we design a lightweight detection head, LT-Detect, using Partial Convolution (PConv) to significantly reduce the classification and localization costs during detection. Finally, we introduce a Receptive Field Block (RFB) in the shallow layers of the backbone to expand the model’s receptive field, enabling effective detection of diseases at various scales. The improved model reduces the number of parameters by 43% and the computational load by 50%. Additionally, it achieves a mean Average Precision (mAP) of 90.9% on a publicly available dataset containing 3641 images of tomato leaf diseases, with only a 0.7% decrease compared to the baseline model. This demonstrates that the model maintains excellent accuracy while being lightweight, making it suitable for rapid detection of tomato leaf diseases.

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Cite This Article

APA Style
He, Z., Tong, M. (2025). LT-YOLO: A lightweight network for detecting tomato leaf diseases. Computers, Materials & Continua, 82(3), 4301–4317. https://doi.org/10.32604/cmc.2025.060550
Vancouver Style
He Z, Tong M. LT-YOLO: A lightweight network for detecting tomato leaf diseases. Comput Mater Contin. 2025;82(3):4301–4317. https://doi.org/10.32604/cmc.2025.060550
IEEE Style
Z. He and M. Tong, “LT-YOLO: A Lightweight Network for Detecting Tomato Leaf Diseases,” Comput. Mater. Contin., vol. 82, no. 3, pp. 4301–4317, 2025. https://doi.org/10.32604/cmc.2025.060550



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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