Cargando…
Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural syst...
Autores principales: | Bellis, Emily S., Hashem, Ahmed A., Causey, Jason L., Runkle, Benjamin R. K., Moreno-García, Beatriz, Burns, Brayden W., Green, V. Steven, Burcham, Timothy N., Reba, Michele L., Huang, Xiuzhen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8984025/ https://www.ncbi.nlm.nih.gov/pubmed/35401643 http://dx.doi.org/10.3389/fpls.2022.716506 |
Ejemplares similares
-
Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging
por: Zhao, Xin, et al.
Publicado: (2019) -
Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery
por: Tatsumi, Kenichi, et al.
Publicado: (2021) -
Applications of Unmanned Aerial Vehicle Based Imagery in Turfgrass Field Trials
por: Zhang, Jing, et al.
Publicado: (2019) -
Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery
por: Han, Seongkyun, et al.
Publicado: (2019) -
Remote Estimation of Rice Yield With Unmanned Aerial Vehicle (UAV) Data and Spectral Mixture Analysis
por: Duan, Bo, et al.
Publicado: (2019)