Cargando…
Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks
Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth st...
Autores principales: | Zhang, Jian, Zhao, Biquan, Yang, Chenghai, Shi, Yeyin, Liao, Qingxi, Zhou, Guangsheng, Wang, Chufeng, Xie, Tianjin, Jiang, Zhao, Zhang, Dongyan, Yang, Wanneng, Huang, Chenglong, Xie, Jing |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298076/ https://www.ncbi.nlm.nih.gov/pubmed/32587594 http://dx.doi.org/10.3389/fpls.2020.00617 |
Ejemplares similares
-
Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery
por: Zhao, Biquan, et al.
Publicado: (2018) -
Automatic counting of rapeseed inflorescences using deep learning method and UAV RGB imagery
por: Li, Jie, et al.
Publicado: (2023) -
Maize Tassel Detection From UAV Imagery Using Deep Learning
por: Alzadjali, Aziza, et al.
Publicado: (2021) -
A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery
por: Huang, Huasheng, et al.
Publicado: (2018) -
Accurate Weed Mapping and Prescription Map Generation Based on Fully Convolutional Networks Using UAV Imagery
por: Huang, Huasheng, et al.
Publicado: (2018)