An intelligent method for detection of small target fungal wheat spores based on an improved YOLOv5 with microscopic images
Abstract Wheat is significantly impacted by fungal diseases, which result in severe economic losses. These diseases result from pathogenic spores invading wheat. Rapid and accurate detection of these spores is essential for post-harvest contamination risk assessment and early warning. Traditional de...
Saved in:
| Main Authors: | Zhizhou Ren, Kun Liang, Yingqi Zhang, Jinpeng Song, Xiaoxiao Wu, Chi Zhang, Xiuming Mei, Yi Zhang, Xin Liu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-08-01
|
| Series: | Plant Methods |
| Online Access: | https://doi.org/10.1186/s13007-025-01436-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Detection of Maize Pathogenic Fungal Spores Based on Deep Learning
by: Yijie Ren, et al.
Published: (2025-08-01) -
Application of microscopic image processing and artificial intelligence detecting and classifying the spores of three novel species of Trichoderma
by: Fatemeh Soltani Nezhad, et al.
Published: (2024-12-01) -
Fungal Spore Seasons Advanced Across the US Over Two Decades of Climate Change
by: Ruoyu Wu, et al.
Published: (2025-07-01) -
IVP-YOLOv5: an intelligent vehicle-pedestrian detection method based on YOLOv5s
by: Yang Sun, et al.
Published: (2023-12-01) -
Electron Beam Irradiation Dose Dependently Damages the Bacillus Spore Coat and Spore Membrane
by: S. E. Fiester, et al.
Published: (2012-01-01)