Hunt Jr et al., 2016 - Google Patents
Insect detection and nitrogen management for irrigated potatoes using remote sensing from small unmanned aircraft systemsHunt Jr et al., 2016
- Document ID
- 9758786413096136575
- Author
- Hunt Jr E
- Rondon S
- Hamm P
- Turner R
- Bruce A
- Brungardt J
- Publication year
- Publication venue
- Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping
External Links
Snippet
Remote sensing with small unmanned aircraft systems (sUAS) has potential applications in agriculture because low flight altitudes allow image acquisition at very high spatial resolution. We set up experiments at the Oregon State University Hermiston Agricultural …
- 240000001016 Solanum tuberosum 0 title abstract description 14
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | High-resolution satellite imagery applications in crop phenotyping: An overview | |
Marcial-Pablo et al. | Estimation of vegetation fraction using RGB and multispectral images from UAV | |
Hunt Jr et al. | What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? | |
Stroppiana et al. | Early season weed mapping in rice crops using multi-spectral UAV data | |
Hunt et al. | Monitoring nitrogen status of potatoes using small unmanned aerial vehicles | |
Stanton et al. | Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment | |
Ahmed et al. | Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle | |
Gómez-Candón et al. | Field phenotyping of water stress at tree scale by UAV-sensed imagery: new insights for thermal acquisition and calibration | |
Thapa et al. | Assessing forest phenology: a multi-scale comparison of near-surface (UAV, spectral reflectance sensor, phenocam) and satellite (MODIS, sentinel-2) remote sensing | |
Hunt Jr et al. | Detection of potato beetle damage using remote sensing from small unmanned aircraft systems | |
Herrmann et al. | Ground-level hyperspectral imagery for detecting weeds in wheat fields | |
Tahir et al. | Real time estimation of chlorophyll content based on vegetation indices derived from multispectral UAV in the kinnow orchard | |
Etienne et al. | Machine learning approaches to automate weed detection by UAV based sensors | |
Sharifi | Estimation of biophysical parameters in wheat crops in Golestan province using ultra-high resolution images | |
Felderhof et al. | Near-infrared imagery from unmanned aerial systems and satellites can be used to specify fertilizer application rates in tree crops | |
Fitzgerald | Characterizing vegetation indices derived from active and passive sensors | |
Wehrhan et al. | Uav-based estimation of carbon exports from heterogeneous soil landscapes—A case study from the carbozalf experimental area | |
Choudhury et al. | Frost damage to maize in northeast India: assessment and estimated loss of yield by hyperspectral proximal remote sensing | |
Hunt Jr et al. | Insect detection and nitrogen management for irrigated potatoes using remote sensing from small unmanned aircraft systems | |
Samseemoung et al. | Oil palm pest infestation monitoring and evaluation by helicopter-mounted, low altitude remote sensing platform | |
Gaertner et al. | Vegetation classification of Coffea on Hawaii Island using WorldView-2 satellite imagery | |
Psomiadis et al. | The role of spatial and spectral resolution on the effectiveness of satellite-based vegetation indices | |
Kawamura et al. | Mapping herbage biomass and nitrogen status in an Italian ryegrass (Lolium multiflorum L.) field using a digital video camera with balloon system | |
Houborg et al. | Detection of chlorophyll and leaf area index dynamics from sub-weekly hyperspectral imagery | |
de Castro et al. | Experimental approach to detect water stress in ornamental plants using sUAS-imagery |