Evaluation of portable tools for fast field assessment of winter wheat grain quality

Bruno Morandin Figueiredo; Mats Söderström; Kristin Persson; T. Börjesson

doi:10.3920/978-90-8686-947-3_16

Evaluation of portable tools for fast field assessment of winter wheat grain quality

Institutionen för mark och miljö

Publikation: Kapitel i bok/rapport/konferenshandling › Konferensartikel i proceedings › Peer review

Sammanfattning

The grain crude protein analysis tool evaluated in this project, combined with satellite imagery has the potential to be an alternative to combine harvester protein mapping devices. Evaluation showed that a calibration is necessary for accurate results. Field-specific and general calibrations were developed bringing MAE to as low as 0.14% for a field-specific calibration. In combination with satellite imagery, protein prediction models were developed using 9 available wavelength bands from a wide range of images throughout the cropping season. Predictions had a MAE as low as 0.10% for a field-specific model.

Originalspråk	Engelska
Titel på värdpublikation	Precision agriculture ’23
Förlag	Wageningen Academic Publishers
Sidor	141-151
Antal sidor	11
ISBN (elektroniskt)	978-90-8686-947-3
ISBN (tryckt)	978-90-8686-393-8
DOI	https://doi.org/10.3920/978-90-8686-947-3_16
Status	Publicerad - 2023

Nyckelord

crude protein
prediction model
remote sensing
satellite
winter wheat

Åtkomst av dokument

10.3920/978-90-8686-947-3_16

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title = "Evaluation of portable tools for fast field assessment of winter wheat grain quality",

abstract = "The grain crude protein analysis tool evaluated in this project, combined with satellite imagery has the potential to be an alternative to combine harvester protein mapping devices. Evaluation showed that a calibration is necessary for accurate results. Field-specific and general calibrations were developed bringing MAE to as low as 0.14\% for a field-specific calibration. In combination with satellite imagery, protein prediction models were developed using 9 available wavelength bands from a wide range of images throughout the cropping season. Predictions had a MAE as low as 0.10\% for a field-specific model.",

keywords = "crude protein, prediction model, remote sensing, satellite, winter wheat, crude protein, prediction model, remote sensing, satellite, winter wheat",

author = "\{Morandin Figueiredo\}, Bruno and Mats S{\"o}derstr{\"o}m and Kristin Persson and T. B{\"o}rjesson",

year = "2023",

doi = "10.3920/978-90-8686-947-3\_16",

language = "English",

isbn = "978-90-8686-393-8",

pages = "141--151",

booktitle = "Precision agriculture {\textquoteright}23",

publisher = "Wageningen Academic Publishers",

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AU - Söderström, Mats

AU - Persson, Kristin

AU - Börjesson, T.

PY - 2023

Y1 - 2023

N2 - The grain crude protein analysis tool evaluated in this project, combined with satellite imagery has the potential to be an alternative to combine harvester protein mapping devices. Evaluation showed that a calibration is necessary for accurate results. Field-specific and general calibrations were developed bringing MAE to as low as 0.14% for a field-specific calibration. In combination with satellite imagery, protein prediction models were developed using 9 available wavelength bands from a wide range of images throughout the cropping season. Predictions had a MAE as low as 0.10% for a field-specific model.

AB - The grain crude protein analysis tool evaluated in this project, combined with satellite imagery has the potential to be an alternative to combine harvester protein mapping devices. Evaluation showed that a calibration is necessary for accurate results. Field-specific and general calibrations were developed bringing MAE to as low as 0.14% for a field-specific calibration. In combination with satellite imagery, protein prediction models were developed using 9 available wavelength bands from a wide range of images throughout the cropping season. Predictions had a MAE as low as 0.10% for a field-specific model.

KW - crude protein

KW - prediction model

KW - remote sensing

KW - satellite

KW - winter wheat

KW - crude protein

KW - prediction model

KW - remote sensing

KW - satellite

KW - winter wheat

UR - https://res.slu.se/id/publ/122672

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DO - 10.3920/978-90-8686-947-3_16

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SP - 141

EP - 151

BT - Precision agriculture ’23

PB - Wageningen Academic Publishers

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