Cargando…
A New Deep Learning-based Dynamic Paradigm Towards Open-World Plant Disease Detection
Plant disease detection has made significant strides thanks to the emergence of deep learning. However, existing methods have been limited to closed-set and static learning settings, where models are trained using a specific dataset. This confinement restricts the model’s adaptability when encounter...
Autores principales: | Dong, Jiuqing, Fuentes, Alvaro, Yoon, Sook, Kim, Hyongsuk, Jeong, Yongchae, Park, Dong Sun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10577201/ https://www.ncbi.nlm.nih.gov/pubmed/37849839 http://dx.doi.org/10.3389/fpls.2023.1243822 |
Ejemplares similares
-
An iterative noisy annotation correction model for robust plant disease detection
por: Dong, Jiuqing, et al.
Publicado: (2023) -
Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning
por: Xu, Mingle, et al.
Publicado: (2023) -
Transfer learning for versatile plant disease recognition with limited data
por: Xu, Mingle, et al.
Publicado: (2022) -
Deep Learning-Based Phenotyping System With Glocal Description of Plant Anomalies and Symptoms
por: Fuentes, Alvaro, et al.
Publicado: (2019) -
Flexible and high quality plant growth prediction with limited data
por: Meng, Yao, et al.
Publicado: (2022)