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This book comprehensively introduces stripe rust disease, its development and its integrated control. Covering the biology, genetics, genome, and functional genomics of the pathogen, it also discusses host and non-host resistance, their interactions with the pathogen and the epidemiology of the disease. It is intended for scientists, postgraduates and undergraduates studying stripe rust, plant pathology, crop breeding, crop protection and agricultural science. It is also a valuable reference book for growers, consultants and administrators in agricultural businesses and education. .

Wheat rusts. --- Stripe rust. --- Yellow rust --- Yellow stripe rust --- Life sciences. --- Agriculture. --- Microbiology. --- Plant pathology. --- Life Sciences. --- Plant Pathology. --- Eukaryotic Microbiology. --- Grasses --- Puccinia striiformis --- Wheat rusts --- Cereal rusts --- Puccinia --- Diseases and pests --- Plant diseases. --- Microbial biology --- Biology --- Microorganisms --- Farming --- Husbandry --- Industrial arts --- Life sciences --- Food supply --- Land use, Rural --- Botany --- Communicable diseases in plants --- Crop diseases --- Crops --- Diseases of plants --- Microbial diseases in plants --- Pathological botany --- Pathology, Vegetable --- Phytopathology --- Plant pathology --- Plants --- Vegetable pathology --- Agricultural pests --- Crop losses --- Diseased plants --- Phytopathogenic microorganisms --- Plant pathologists --- Plant quarantine --- Pathology --- Diseases --- Wounds and injuries

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This book compiles a set of original and innovative papers included in the Special Issue on UAVs for vegetation monitoring, which proves the wide scope of UAVs in very diverse vegetation applications, both in agricultural and forestry scenarios, ranging from the characterization of relevant vegetation features to the detection of plant or crop stressors. New methods and techniques are developed and applied to diverse vegetation scenarios to meet the main challenge of sustainability.

Research & information: general --- 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 --- 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

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• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book compiles a set of original and innovative papers included in the Special Issue on UAVs for vegetation monitoring, which proves the wide scope of UAVs in very diverse vegetation applications, both in agricultural and forestry scenarios, ranging from the characterization of relevant vegetation features to the detection of plant or crop stressors. New methods and techniques are developed and applied to diverse vegetation scenarios to meet the main challenge of sustainability.

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