ArticleFrontiers in plant science2024
SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Lightweight attention enhanced YOLOv11 for accurate multi class detection of brinjal diseases.Scientific reports · 2026Article
- Digital Twin-Based Improved YOLOv8 Algorithm for Micro-Defect Detection of Labyrinth Drip Emitters in High-Speed Agricultural Production Lines.Sensors (Basel, Switzerland) · 2026Article
- YOLO-based deep learning framework for real-time multi-class plant health monitoring in precision agriculture.Scientific reports · 2026Article
- Article
- Anisotropic boundary-aware detection for cotton leaf diseases with boundary-decoupled regression and lightweight feature adaptation.Frontiers in plant science · 2026Article
- Deep learning enables edge deployment for citrus leaf disease recognition in natural orchard scenes.Frontiers in plant science · 2026Article
- LeafDet: A lightweight and interpretable deep learning framework for tomato leaf disease detection.PloS one · 2026Article
- Improved YOLOv7 enhances identification ofFrontiers in plant science · 2026Article
- A resource-efficient framework for plant disease classification: integrating reduced-order modeling with treatment-based label engineering.Frontiers in plant science · 2026Article
- Concrete slump detection based on light-AWGAM-YOLOv8n.Scientific reports · 2025Article
- Intelligent identification method for rice seedling growth stages and its application in laser supplementary lighting control research.Scientific reports · 2025Article
- YOLO-PLNet: a lightweight real-time detection model for peanut leaf diseases based on edge deployment.Frontiers in plant science · 2025Article
- Lightweight grading method for potato late blight severity based on enhanced YOLOv8-Unet3Plus network.Frontiers in plant science · 2025Article
- Full-time sequence assessment of okra seedling vigor under salt stress based on leaf area and leaf growth rate estimation using the YOLOv11-HSECal instance segmentation model.Frontiers in plant science · 2025Article
- CNATNet: a convolution-attention hybrid network for safflower classification.Frontiers in plant science · 2025Article
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3 authors.
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Abstract
Plant disease detection remains a significant challenge, necessitating innovative approaches to enhance detection efficiency and accuracy. This study proposes an improved YOLOv8 model, SerpensGate-YOLOv8, specifically designed for plant disease detection tasks. Key enhancements include the incorporation of Dynamic Snake Convolution (DySnakeConv) into the C2F module, which improves the detection of intricate features in complex structures, and the integration of the SPPELAN module, combining Spatial Pyramid Pooling (SPP) and Efficient Local Aggregation Network (ELAN) for superior feature extraction and fusion. Additionally, an innovative Super Token Attention (STA) mechanism was introduced to strengthen global feature modeling during the early stages of the network. The model leverages the PlantDoc dataset, a highly generalizable dataset containing 2,598 images across 13 plant species and 27 classes (17 diseases and 10 healthy categories). With these improvements, the model achieved a Precision of 0.719. Compared to the original YOLOv8, the mean Average Precision (mAP@0.5) improved by 3.3%, demonstrating significant performance gains. The results indicate that SerpensGate-YOLOv8 is a reliable and efficient solution for plant disease detection in real-world agricultural environments.
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