[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Resende et al., 2024 - Google Patents

Satellite-enabled enviromics to enhance crop improvement

Resende et al., 2024

View HTML
Document ID
6396416796507709392
Author
Resende R
Hickey L
Amaral C
Peixoto L
Marcatti G
Xu Y
Publication year
Publication venue
Molecular Plant

External Links

Snippet

Enviromics refers to the characterization of micro-and macroenvironments based on large- scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions …
Continue reading at www.cell.com (HTML) (other versions)

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
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
    • G01N2021/3155—Measuring in two spectral ranges, e.g. UV and visible
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/40—Scaling the whole image or part thereof
    • G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
    • G06T3/4061—Super resolution, i.e. output image resolution higher than sensor resolution by injecting details from a different spectral band

Similar Documents

Publication Publication Date Title
Fei et al. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat
Valbuena et al. Standardizing ecosystem morphological traits from 3D information sources
Lu et al. Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system
Simic Milas et al. The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images
Liu et al. The Heihe Integrated Observatory Network: A basin‐scale land surface processes observatory in China
dela Torre et al. Remote sensing-based estimation of rice yields using various models: A critical review
Pullanagari et al. Mapping of macro and micro nutrients of mixed pastures using airborne AisaFENIX hyperspectral imagery
Wimberly et al. Satellite observations and malaria: new opportunities for research and applications
Liu et al. Analysis of the urban heat island effect in Shijiazhuang, China using satellite and airborne data
Malenovský et al. Variability and uncertainty challenges in scaling imaging spectroscopy retrievals and validations from leaves up to vegetation canopies
Abdulraheem et al. Advancement of remote sensing for soil measurements and applications: A comprehensive review
Ali et al. Estimation and validation of RapidEye-based time-series of leaf area index for winter wheat in the Rur catchment (Germany)
Zhu et al. Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data
McCabe et al. High-resolution sensing for precision agriculture: from Earth-observing satellites to unmanned aerial vehicles
Abdelbaki et al. Comparison of crop trait retrieval strategies using UAV-based VNIR hyperspectral imaging
Wang et al. Classification of rice yield using UAV-based hyperspectral imagery and lodging feature
Ji et al. Crop yield estimation at field scales by assimilating time series of Sentinel-2 data into a modified CASA-WOFOST coupled model
Tan et al. Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm
Kganyago et al. Optical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture
Wang et al. Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning
Resende et al. Satellite-enabled enviromics to enhance crop improvement
Piazzi et al. Cross-country assessment of H-SAF snow products by Sentinel-2 imagery validated against in-situ observations and webcam photography
Suradhaniwar et al. Geo-ICDTs: Principles and applications in agriculture
Rumora et al. Contemporary comparative assessment of atmospheric correction influence on radiometric indices between Sentinel-2A and Landsat 8 imagery
Vijayasekaran SEN2-AGRI–Crop type mapping pilot study using sentinel-2 satellite imagery in India