top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.
Genetics and Breeding for Productivity Traits in Forage and Bioenergy Grasses
Genetics and Breeding for Productivity Traits in Forage and Bioenergy Grasses
Autore Kevin F. Smith (Ed.)
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2017
Descrizione fisica 1 electronic resource (VIII, 204 p.)
Soggetto non controllato forage grass
biomass
protein content
lignocellulosic content
persistence
seasonal yield
soluble carbohydrates
bioenergy grass
herbage digestibility
biofermentation
ISBN 3-03842-543-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Bionanotechnology to Save the Environment
Record Nr. UNINA-9910227344203321
Kevin F. Smith (Ed.)  
MDPI - Multidisciplinary Digital Publishing Institute, 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
UAVs for Vegetation Monitoring
UAVs for Vegetation Monitoring
Autore de Castro Megías Ana
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Descrizione fisica 1 electronic resource (452 p.)
Soggetto topico Research & information: general
Soggetto non controllato UAS
UAV
vegetation cover
multispectral
land cover
forest
Acacia
Indonesia
tropics
vegetation ground cover
vegetation indices
agro-environmental measures
olive groves
southern Spain
sUAS
water stress
ornamental
container-grown
artificial intelligence
machine learning
deep learning
neural network
visual recognition
precision agriculture
canopy cover
image analysis
crop mapping
evapotranspiration (ET)
GRAPEX
remote sensing
Two Source Energy Balance model (TSEB)
contextual spatial domain/resolution
data aggregation
eddy covariance (EC)
Fusarium wilt
crop disease
banana
multispectral remote sensing
purple rapeseed leaves
unmanned aerial vehicle
U-Net
plant segmentation
nitrogen stress
Glycine max
RGB
canopy height
close remote sensing
growth model
curve fitting
NDVI
solar zenith angle
flight altitude
time of day
operating parameters
CNN
Faster RCNN
SSD
Inception v2
patch-based CNN
MobileNet v2
detection performance
inference time
disease detection
cotton root rot
plant-level
single-plant
plant-by-plant
classification
UAV remote sensing
crop monitoring
RGB imagery
multispectral imagery
century-old biochar
semantic segmentation
random forest
crop canopy
multispectral image
chlorophyll content
remote sensing technique
individual plant segmentation
plant detection
transfer learning
maize tassel
tassel branch number
convolution neural network
VGG16
plant nitrogen estimation
vegetation index
image segmentation
transpiration
method comparison
oil palm
multiple linear regression
support vector machine
artificial neural network
UAV hyperspectral
wheat yellow rust
disease monitoring
texture
spatial resolution
RGB camera
thermal camera
drought tolerance
forage grass
HSV
CIELab
broad-sense heritability
phenotyping gap
high throughput field phenotyping
UAV digital images
winter wheat biomass
multiscale textures
red-edge spectra
least squares support vector machine
variable importance
drone
hyperspectral
thermal
nutrient deficiency
weed detection
disease diagnosis
plant trails
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557661103321
de Castro Megías Ana  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui