ArticleFrontiers in plant science2023
Improved YOLOX-Tiny network for detection of tobacco brown spot disease.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 33 citations in OpenAlex.
- A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.Journal of imaging · 2026Review
- A robust cross-crop disease detection framework based on SIS-YOLOv11 with climate-adaptive mechanisms.PloS one · 2026Article
- A study on plant disease and pest detection and counting based on multi-scale enhancement and cross-scale fusion.Frontiers in plant science · 2026Article
- Research on non-tobacco related materials recognition method based on YOLOv8.Scientific reports · 2025Article
- A privacy-protecting eggplant disease detection framework based on the YOLOv11n-12D model.Frontiers in plant science · 2025Article
- Metabolomics and transcriptomics combined with physiology reveal key metabolic pathway responses in tobacco roots exposed to NaHS.BMC plant biology · 2024Article
- Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8.Frontiers in plant science · 2024Article
- Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition.Plant phenomics (Washington, D.C.) · 2024Article
- ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases.Frontiers in plant science · 2023Article
- Improved EfficientNet for corn disease identification.Frontiers in plant science · 2023Article
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Authors and funding
13 authors at 4 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Tobacco brown spot disease caused by Methods: Here, we propose an improved YOLOX-Tiny network, named YOLO-Tobacco, for the detection of tobacco brown spot disease under open-field scenarios. Aiming to excavate valuable disease features and enhance the integration of different levels of features, thereby improving the ability to detect dense disease spots at different scales, we introduced hierarchical mixed-scale units (HMUs) in the neck network for information interaction and feature refinement between channels. Furthermore, in order to enhance the detection of small disease spots and the robustness of the network, we also introduced convolutional block attention modules (CBAMs) into the neck network. Results: As a result, the YOLO-Tobacco network achieved an average precision (AP) of 80.56% on the test set. The AP was 3.22%, 8.99%, and 12.03% higher than that obtained by the classic lightweight detection networks YOLOX-Tiny network, YOLOv5-S network, and YOLOv4-Tiny network, respectively. In addition, the YOLO-Tobacco network also had a fast detection speed of 69 frames per second (FPS). Discussion: Therefore, the YOLO-Tobacco network satisfies both the advantages of high detection accuracy and fast detection speed. It will likely have a positive impact on early monitoring, disease control, and quality assessment in diseased tobacco plants.
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