Cargando…
Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases
Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intellige...
Autores principales: | Zhu, Ruilin, Zou, Hongyan, Li, Zhenye, Ni, Ruitao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824080/ https://www.ncbi.nlm.nih.gov/pubmed/36616300 http://dx.doi.org/10.3390/plants12010169 |
Ejemplares similares
-
Lightweight Algorithm for Apple Detection Based on an Improved YOLOv5 Model
por: Sun, Yu, et al.
Publicado: (2023) -
EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5
por: Zhu, Shisong, et al.
Publicado: (2023) -
Fruit Detection and Counting in Apple Orchards Based on Improved Yolov7 and Multi-Object Tracking Methods
por: Hu, Jing, et al.
Publicado: (2023) -
Red and Blue Netting Alters Leaf Morphological and Physiological Characteristics in Apple Trees
por: Bastías, Richard M., et al.
Publicado: (2021) -
First Detection and Molecular Characterization of Apple Stem Grooving Virus, Apple Chlorotic Leaf Spot Virus, and Apple Hammerhead Viroid in Loquat in Spain
por: Canales, Celia, et al.
Publicado: (2021)