The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/f0fd8497-0622-460b-92e2-721001f5aecf

Maestrini, Bernardo; Michielsen, Jean-Marie; Boersma, Sjoerd; Wan, Shumin; Beniers, Annelies et. al. (2024): Data underlying the VIOLA model for potato stress classification using NDVI. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/f0fd8497-0622-460b-92e2-721001f5aecf.v1

Dataset

• Crop and Pasture Production
• Agricultural and Veterinary Sciences

anomaly detection, crop anomalies, nitrogen stress, potato, remote sensing, stress classification, water stress, weeds

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2023-2024

CC0

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by Bernardo Maestrini , Jean-Marie Michielsen , Sjoerd Boersma , Shumin Wan , Annelies Beniers , Geert Kessel , Frank Hollewand , Mohamad Shamsi , Manya Afonso

This data were generated in the context of the Kennis Basis 38 Program on Data Driven and High Tech - use case on AI on arable and animal farms.

The objective of the experiment was to evaluate the effect of various potato stress factors on canopy reflectance.

This data have been collected to validate a model trained on a synthetic dataset.

The experiment has been conducted for two growing seasons: 2023 and 2024.  

The experiment was a factorial combination of 2 cultivars (Frieslander early cultivar and Avenger late cultivar) x 5 treatments. In 2023 we had no replicates whereas in 2024 we had 3 replicates.

The treatments applied to this experiment were:

* No stress

* Nitrogen stress

* Poor potato emergence

* Phytophtora (only in 2024)

* Weeds                                                                                                                     

The original experiment was supposed to inclide also a water stress treatment by irrigating all fields except for the water stress treatment, however 2023 and 2024 were two wet years and thus irrigation was not needed, this implies that we have two controls treatments instead of one. We measured reflectance using hyperspectral point sensor (TEC5) and drone. For the drone here we report the average reflectance by plot, date and band, drone images can be made available upon request.

• 2024-12-23 first online, published, posted

4TU.ResearchData

csv

• AI in animal and arable systems (grant code KB-38-001-002) [more info...] LVVN
• the Wageningen Data Driven Discoveries in Changing Climate (D3-C2) [more info...] the Wageningen Data Driven Discoveries in Changing Climate (D3-C2)

Plant science group, Agrosystems Research, Wageningen University and Research