Site-Specific Seed Yield Prediction of Red Fescue (Festuca rubra L.)
Publikation: Bidrag til bog/antologi/rapport › Konferenceabstrakt i proceedings › Forskning
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Site-Specific Seed Yield Prediction of Red Fescue (Festuca rubra L.). / Andreasen, Christian; Rasmussen, Jesper; Bitarafan, Z.
Unleashing the potential of precision agriculture - Book of Abstracts (Posters): 14th European Conference on Precision Agriculture. red. / Maurizio Canavari; Giuliano Vitali; Michele Mattetti. Universita di Bologna, 2023. s. 119-120 P59.Publikation: Bidrag til bog/antologi/rapport › Konferenceabstrakt i proceedings › Forskning
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TY - ABST
T1 - Site-Specific Seed Yield Prediction of Red Fescue (Festuca rubra L.)
AU - Andreasen, Christian
AU - Rasmussen, Jesper
AU - Bitarafan, Z.
N1 - Publisher Copyright: © 2023 by the authors.
PY - 2023
Y1 - 2023
N2 - Yield maps give farmers information about growth conditions and can be a tool for site-specific crop management. Combine harvesters may provide farmers with detailed yield maps if there is a constant flow of a certain amount of biomass through the yield sensor. This is unachievable for grass seeds because the weight of the intake is generally too small to record the variation. Therefore, there is a need to find another way to make grass seed yield maps. We studied seed yield variation in two red fescue (Festuca rubra) fields with variation in management and soil fertility, respectively. We estimated five vegetation indices (VI) based on RGB images taken from a drone to describe yield variation, and trained prediction models based on relatively few harvested plots. Only results from the VI showing the strongest correlation between the index and the yield are presented (Normalized Excess Green Index (ExG) and Normalized Green/Red Difference Index (NGRDI)). The study indicates that it is possible to predict the yield variation in a grass field based on relatively few harvested plots, provided the plots represent contrasting yield levels. The prediction errors in yield (RMSE) ranged from 171 kg ha−1 to 231 kg ha−1, with no clear influence of the size of the training data set. Using random selection of plots instead of selecting plots representing contrasting yield levels resulted in slightly better predictions when evaluated on an average of ten random selections. However, using random selection of plots came with a risk of poor predictions due to the occasional lack of correlation between yield and VI. The exact timing of unmanned aerial vehicles (UAVs) image capture showed to be unimportant in the weeks before harvest.
AB - Yield maps give farmers information about growth conditions and can be a tool for site-specific crop management. Combine harvesters may provide farmers with detailed yield maps if there is a constant flow of a certain amount of biomass through the yield sensor. This is unachievable for grass seeds because the weight of the intake is generally too small to record the variation. Therefore, there is a need to find another way to make grass seed yield maps. We studied seed yield variation in two red fescue (Festuca rubra) fields with variation in management and soil fertility, respectively. We estimated five vegetation indices (VI) based on RGB images taken from a drone to describe yield variation, and trained prediction models based on relatively few harvested plots. Only results from the VI showing the strongest correlation between the index and the yield are presented (Normalized Excess Green Index (ExG) and Normalized Green/Red Difference Index (NGRDI)). The study indicates that it is possible to predict the yield variation in a grass field based on relatively few harvested plots, provided the plots represent contrasting yield levels. The prediction errors in yield (RMSE) ranged from 171 kg ha−1 to 231 kg ha−1, with no clear influence of the size of the training data set. Using random selection of plots instead of selecting plots representing contrasting yield levels resulted in slightly better predictions when evaluated on an average of ten random selections. However, using random selection of plots came with a risk of poor predictions due to the occasional lack of correlation between yield and VI. The exact timing of unmanned aerial vehicles (UAVs) image capture showed to be unimportant in the weeks before harvest.
KW - aerial images
KW - creeping red fescue
KW - crop color
KW - drone imaging
KW - local regression models
KW - slender creeping red fescue
M3 - Conference abstract in proceedings
AN - SCOPUS:85149121554
SP - 119
EP - 120
BT - Unleashing the potential of precision agriculture - Book of Abstracts (Posters)
A2 - Canavari, Maurizio
A2 - Vitali, Giuliano
A2 - Mattetti, Michele
PB - Universita di Bologna
ER -
ID: 360391990