Parmley et al., 2019 - Google Patents
Machine learning approach for prescriptive plant breedingParmley 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 …
- 238000010801 machine learning 0 title abstract description 28
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
- G06Q10/0639—Performance analysis
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Investment, e.g. financial instruments, portfolio management or fund management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- 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
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 |