Sharma et al., 2015 - Google Patents

High‐throughput phenotyping of cotton in multiple irrigation environments

Sharma et al., 2015

View PDF
Document ID
7628969475194477342
Author
Sharma B
Ritchie G
Publication year
Publication venue
Crop Science

External Links

Snippet

Rapid screening of plant growth provides additional phenological information for cotton (Gossypium hirsutum L.), which may then be linked to productivity analysis in breeding and agronomy. We tested automated measurements of plant height, ground cover fraction (GCF) …
Continue reading at ttu-ir.tdl.org (PDF) (other versions)

Classifications

    • 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
    • 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
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups

Similar Documents

Publication Publication Date Title
Sharma et al. High‐throughput phenotyping of cotton in multiple irrigation environments
Morlin Carneiro et al. Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors
Araus et al. Field high-throughput phenotyping: the new crop breeding frontier
Goffart et al. Potato crop nitrogen status assessment to improve N fertilization management and efficiency: past–present–future
Solari et al. Active sensor reflectance measurements of corn nitrogen status and yield potential
Osborne et al. Use of spectral radiance to estimate in‐season biomass and grain yield in nitrogen‐and water‐stressed corn
Yang et al. Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder
Raj et al. Precision agriculture and unmanned aerial Vehicles (UAVs)
Barker et al. Using active canopy sensors to quantify corn nitrogen stress and nitrogen application rate
Reyniers et al. A linear model to predict with a multi‐spectral radiometer the amount of nitrogen in winter wheat
Wu et al. Comparison of machine learning algorithms for classification of LiDAR points for characterization of canola canopy structure
Monneveux et al. Drought and heat tolerance evaluation in potato (Solanum tuberosum L.)
Gutierrez et al. Association of spectral reflectance indices with plant growth and lint yield in upland cotton
Natarajan et al. High-throughput phenotyping of indirect traits for early-stage selection in sugarcane breeding
Girma et al. Mid-season prediction of wheat-grain yield potential using plant, soil, and sensor measurements
Crain et al. Utilizing high‐throughput phenotypic data for improved phenotypic selection of stress‐adaptive traits in wheat
Bronson et al. Cotton canopy reflectance at landscape scale as affected by nitrogen fertilization
Hoffmann et al. Estimation of leaf area index of Beta vulgaris L. based on optical remote sensing data
Cao et al. Inversion modeling of japonica rice canopy chlorophyll content with UAV hyperspectral remote sensing
Cozzolino The role of near-infrared sensors to measure water relationships in crops and plants
Inostroza et al. Using aerial images and canopy spectral reflectance for high‐throughput phenotyping of white clover
Kelly et al. By-plant prediction of corn (Zea mays L.) grain yield using height and stalk diameter
Zhang et al. Vis/NIR reflectance spectroscopy for hybrid rice variety identification and chlorophyll content evaluation for different nitrogen fertilizer levels
Hoyos‐Villegas et al. Relationships among vegetation indices derived from aerial photographs and soybean growth and yield
Zhao et al. Physiological and yield characteristics of 18 sugarcane genotypes grown on a sand soil