Nothing Special   »   [go: up one dir, main page]

Parmley et al., 2019 - Google Patents

Machine learning approach for prescriptive plant breeding

Parmley et al., 2019

View HTML
Document ID
10008668032326271109
Author
Parmley K
Higgins R
Ganapathysubramanian B
Sarkar S
Singh A
Publication year
Publication venue
Scientific reports

External Links

Snippet

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro …
Continue reading at www.nature.com (HTML) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0639Performance analysis
    • G06Q10/06393Score-carding, benchmarking or key performance indicator [KPI] analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06Investment, e.g. financial instruments, portfolio management or fund management
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/314Investigating 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
    • G01N2021/3155Measuring in two spectral ranges, e.g. UV and visible

Similar Documents

Publication Publication Date Title
Parmley et al. Machine learning approach for prescriptive plant breeding
Parmley et al. Development of optimized phenomic predictors for efficient plant breeding decisions using phenomic-assisted selection in soybean
Sandhu et al. Multitrait machine‐and deep‐learning models for genomic selection using spectral information in a wheat breeding program
Selvaraj et al. Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)
Fiorani et al. Future scenarios for plant phenotyping
Rahaman et al. Advanced phenotyping and phenotype data analysis for the study of plant growth and development
Heckmann et al. Machine learning techniques for predicting crop photosynthetic capacity from leaf reflectance spectra
Chen et al. Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis
Prasad et al. Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices
Liu et al. Estimation of plant and canopy architectural traits using the digital plant phenotyping platform
Mertens et al. Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology
Guo et al. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques
Crain et al. Utilizing high‐throughput phenotypic data for improved phenotypic selection of stress‐adaptive traits in wheat
Haghighattalab et al. Application of geographically weighted regression to improve grain yield prediction from unmanned aerial system imagery
Tourne et al. Strategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan‐Amazonia
Tripodi et al. Digital applications and artificial intelligence in agriculture toward next-generation plant phenotyping
Liu et al. Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery
Morisse et al. A European perspective on opportunities and demands for field-based crop phenotyping
Marko et al. Portfolio optimization for seed selection in diverse weather scenarios
Lou et al. Hyperspectral remote sensing to assess weed competitiveness in maize farmland ecosystems
Chang et al. A data-driven crop model for maize yield prediction
Paleari et al. Estimating crop nutritional status using smart apps to support nitrogen fertilization. A case study on paddy rice
Bustos-Korts et al. Identification of environment types and adaptation zones with self-organizing maps; applications to sunflower multi-environment data in Europe
Zhi et al. Estimating photosynthetic attributes from high-throughput canopy hyperspectral sensing in sorghum
DeSalvio et al. Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms