AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search Algorithm
Grapevine diseases caused by fungal pathogens pose a significant threat to viticulture, leading to considerable economic losses and reduced productivity. Early, intelligent detection of fungal spores is vital for effective disease management. This study presents a high-accuracy classification model...
Saved in:
| Main Authors: | Xin Shi, Seyed Mohamad Javidan, Yiannis Ampatzidis, Zhao Zhang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
|
| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S277237552500262X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Host Jumps and Pathogenicity of Botryosphaeriaceae Species on Grapevines (<i>Vitis vinifera</i>) in Chile
by: Yadira Hernández, et al.
Published: (2025-02-01) -
Plant- and Microbial-Based Organic Disease Management for Grapevines: A Review
by: Mereke Alimzhanova, et al.
Published: (2025-04-01) -
A Novel Hybrid Technique for Detecting and Classifying Hyperspectral Images of Tomato Fungal Diseases Based on Deep Feature Extraction and Manhattan Distance
by: Guifu Ma, et al.
Published: (2025-07-01) -
Fungal community dynamics and anthocyanin profiling of grapevine leaves in a vineyard affected by esca
by: Giovanni Del Frari, et al.
Published: (2025-03-01) -
Nursery Origin and Propagation Stage Influence the Endophytic Fungal Communities Inhabiting Grapevine Planting Stocks
by: Jadran F. Garcia, et al.
Published: (2025-06-01)