Dealing with HTTP data in modern crop breeding programs
2020
Преузимање 🢃
Аутори
Marjanović-Jeromela, AnaZorić, Miroslav
Rajković, Dragana
Terzić, Sreten
Kondić-Špika, Ankica
Miladinović, Dragana
Cvejić, Sandra
Đorđević, Vuk
Vollman, Johann
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Modern crop breeding programs are data-driven. A breeder’s decisions are based on the prediction of the genotype performance from a large number of field trials. These trials should account for environmental variability of the target region, and more importantly, they should possess a high degree of accuracy. In recent years, different robotic and sensor technologies for collecting high-throughput field-based plant phenotyping (HTTP) data have been developed. Thereby, the possibility for gaining higher overall precision, as well as data and decision accuracy from crop breeding field trials was gained. Prediction of end-of-season yield and quality will become faster with the use of cameras for hyperspectral imaging, which is important for large scale producers. Comparing big sets of images generated in the field with results of classical chemical analyses serves as an advanced crop quality prediction tool for breeders. Important steps in such data analysis are calibration, noise reducti...on and the search for the most significant relations. Nevertheless, assessing phenotypic traits within genetic collections is made more accurate with the aid of phenotyping platforms that record plant growth from germ to seed. Like many types of phenotypic data, HTTP data collected from the images may also have some amount of unknown variability.
Кључне речи:
modern breeding / robotic / sensor technologies / hyperspectral imaging / HTTP data / prediction model / statistical modelИзвор:
Book of Abstracts, 3rd edition, Digital breeding International Symposium of the Society for Plant Breeding e.V. (GPZ) in cooperation with Saatgut Austria, 11-13. 02. 2020., Tulln, Austria, 2020, 116-116Издавач:
- Goettingen : Gesellschaft fuer Pflanzenzuechtung (GPZ)
Колекције
Институција/група
FiVeRTY - CONF AU - Marjanović-Jeromela, Ana AU - Zorić, Miroslav AU - Rajković, Dragana AU - Terzić, Sreten AU - Kondić-Špika, Ankica AU - Miladinović, Dragana AU - Cvejić, Sandra AU - Đorđević, Vuk AU - Vollman, Johann PY - 2020 UR - http://fiver.ifvcns.rs/handle/123456789/2197 AB - Modern crop breeding programs are data-driven. A breeder’s decisions are based on the prediction of the genotype performance from a large number of field trials. These trials should account for environmental variability of the target region, and more importantly, they should possess a high degree of accuracy. In recent years, different robotic and sensor technologies for collecting high-throughput field-based plant phenotyping (HTTP) data have been developed. Thereby, the possibility for gaining higher overall precision, as well as data and decision accuracy from crop breeding field trials was gained. Prediction of end-of-season yield and quality will become faster with the use of cameras for hyperspectral imaging, which is important for large scale producers. Comparing big sets of images generated in the field with results of classical chemical analyses serves as an advanced crop quality prediction tool for breeders. Important steps in such data analysis are calibration, noise reduction and the search for the most significant relations. Nevertheless, assessing phenotypic traits within genetic collections is made more accurate with the aid of phenotyping platforms that record plant growth from germ to seed. Like many types of phenotypic data, HTTP data collected from the images may also have some amount of unknown variability. PB - Goettingen : Gesellschaft fuer Pflanzenzuechtung (GPZ) C3 - Book of Abstracts, 3rd edition, Digital breeding International Symposium of the Society for Plant Breeding e.V. (GPZ) in cooperation with Saatgut Austria, 11-13. 02. 2020., Tulln, Austria T1 - Dealing with HTTP data in modern crop breeding programs EP - 116 SP - 116 UR - https://hdl.handle.net/21.15107/rcub_fiver_2197 ER -
@conference{ author = "Marjanović-Jeromela, Ana and Zorić, Miroslav and Rajković, Dragana and Terzić, Sreten and Kondić-Špika, Ankica and Miladinović, Dragana and Cvejić, Sandra and Đorđević, Vuk and Vollman, Johann", year = "2020", abstract = "Modern crop breeding programs are data-driven. A breeder’s decisions are based on the prediction of the genotype performance from a large number of field trials. These trials should account for environmental variability of the target region, and more importantly, they should possess a high degree of accuracy. In recent years, different robotic and sensor technologies for collecting high-throughput field-based plant phenotyping (HTTP) data have been developed. Thereby, the possibility for gaining higher overall precision, as well as data and decision accuracy from crop breeding field trials was gained. Prediction of end-of-season yield and quality will become faster with the use of cameras for hyperspectral imaging, which is important for large scale producers. Comparing big sets of images generated in the field with results of classical chemical analyses serves as an advanced crop quality prediction tool for breeders. Important steps in such data analysis are calibration, noise reduction and the search for the most significant relations. Nevertheless, assessing phenotypic traits within genetic collections is made more accurate with the aid of phenotyping platforms that record plant growth from germ to seed. Like many types of phenotypic data, HTTP data collected from the images may also have some amount of unknown variability.", publisher = "Goettingen : Gesellschaft fuer Pflanzenzuechtung (GPZ)", journal = "Book of Abstracts, 3rd edition, Digital breeding International Symposium of the Society for Plant Breeding e.V. (GPZ) in cooperation with Saatgut Austria, 11-13. 02. 2020., Tulln, Austria", title = "Dealing with HTTP data in modern crop breeding programs", pages = "116-116", url = "https://hdl.handle.net/21.15107/rcub_fiver_2197" }
Marjanović-Jeromela, A., Zorić, M., Rajković, D., Terzić, S., Kondić-Špika, A., Miladinović, D., Cvejić, S., Đorđević, V.,& Vollman, J.. (2020). Dealing with HTTP data in modern crop breeding programs. in Book of Abstracts, 3rd edition, Digital breeding International Symposium of the Society for Plant Breeding e.V. (GPZ) in cooperation with Saatgut Austria, 11-13. 02. 2020., Tulln, Austria Goettingen : Gesellschaft fuer Pflanzenzuechtung (GPZ)., 116-116. https://hdl.handle.net/21.15107/rcub_fiver_2197
Marjanović-Jeromela A, Zorić M, Rajković D, Terzić S, Kondić-Špika A, Miladinović D, Cvejić S, Đorđević V, Vollman J. Dealing with HTTP data in modern crop breeding programs. in Book of Abstracts, 3rd edition, Digital breeding International Symposium of the Society for Plant Breeding e.V. (GPZ) in cooperation with Saatgut Austria, 11-13. 02. 2020., Tulln, Austria. 2020;:116-116. https://hdl.handle.net/21.15107/rcub_fiver_2197 .
Marjanović-Jeromela, Ana, Zorić, Miroslav, Rajković, Dragana, Terzić, Sreten, Kondić-Špika, Ankica, Miladinović, Dragana, Cvejić, Sandra, Đorđević, Vuk, Vollman, Johann, "Dealing with HTTP data in modern crop breeding programs" in Book of Abstracts, 3rd edition, Digital breeding International Symposium of the Society for Plant Breeding e.V. (GPZ) in cooperation with Saatgut Austria, 11-13. 02. 2020., Tulln, Austria (2020):116-116, https://hdl.handle.net/21.15107/rcub_fiver_2197 .