Published December 13, 2024
| Version v1
Dataset
Open
Image-based yield prediction for tall fescue using random forests and convolutional neural networks
Authors/Creators
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Ghysels, Sarah
(Researcher)1
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Maenhout, Steven
(Supervisor)1, 2
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Dubey, Reena
(Data collector)2
- Goethals, Michaël (Data collector)1
- De Wagter, Pieter (Data collector)1
- Van Peteghem, Franky (Data collector)1
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Reheul, Dirk
(Data collector)1
- Van Rysselberghe, Margot (Data collector)1
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Dewitte, Kevin
(Data collector)1
-
1.
Ghent University
- 2. Progeno
Description
This record contains all data used in the research paper 'Image-based yield prediction for tall fescue using random forests and convolutional neural networks' by Ghysels, S., De Baets, B., Reheul, D. and Maenhout, S. 'Train_dataset.zip' and 'Test_dataset.zip' contain the RGB images of individual tall fescue plants, split into a training set and test set respectively. 'Multigras_data.csv' contains the dry matter yield measurements ('DMY (kg/ha)'), the breeder's evaluation scores ('Score MG') and the location of each individual plant on the field ('Blok_Rij_Plantnr', meaning Block-row-column).
Files
Multigras_data.csv
Additional details
Related works
- Is source of
- Journal article: 10.3389/fpls.2025.1549099 (DOI)
Software
- Repository URL
- https://github.com/SarahGhysels/Image-based-yield-prediction-for-tall-fescue
- Programming language
- Python