Cargando…
The Gray Mold Spore Detection of Cucumber Based on Microscopic Image and Deep Learning
Rapid and accurate detection of pathogen spores is an important step to achieve early diagnosis of diseases in precision agriculture. Traditional detection methods are time-consuming, laborious, and subjective, and image processing methods mainly rely on manually designed features that are difficult...
Autores principales: | Li, Kaiyu, Zhu, Xinyi, Qiao, Chen, Zhang, Lingxian, Gao, Wei, Wang, Yong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013786/ https://www.ncbi.nlm.nih.gov/pubmed/36930758 http://dx.doi.org/10.34133/plantphenomics.0011 |
Ejemplares similares
-
Attention-optimized DeepLab V3 + for automatic estimation of cucumber disease severity
por: Li, Kaiyu, et al.
Publicado: (2022) -
A Rapid Detection Method for Tomato Gray Mold Spores in Greenhouse Based on Microfluidic Chip Enrichment and Lens-Less Diffraction Image Processing
por: Wang, Yafei, et al.
Publicado: (2021) -
Sea Cucumber Detection Algorithm Based on Deep Learning
por: Zhang, Lan, et al.
Publicado: (2022) -
Early bread mold detection through microscopic images using convolutional neural network
por: Treepong, Panisa, et al.
Publicado: (2023) -
Genome sequence and spore germination-associated transcriptome analysis of Corynespora cassiicola from cucumber
por: Gao, Shigang, et al.
Publicado: (2020)