TY - DATA T1 - Data underlying the VIOLA model for potato stress classification using NDVI PY - 2024/12/23 AU - Bernardo Maestrini AU - Jean-Marie Michielsen AU - Sjoerd Boersma AU - Shumin Wan AU - Annelies Beniers AU - Geert Kessel AU - Frank Hollewand AU - Mohamad Shamsi AU - Manya Afonso UR - DO - 10.4121/f0fd8497-0622-460b-92e2-721001f5aecf.v1 KW - potato KW - anomaly detection KW - stress classification KW - crop anomalies KW - remote sensing KW - weeds KW - nitrogen stress KW - water stress N2 - <p>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.</p><p>The objective of the experiment was to evaluate the effect of various potato stress factors on canopy reflectance.</p><p>This data have been collected to validate a model trained on a synthetic dataset.</p><p>The experiment has been conducted for two growing seasons: 2023 and 2024. </p><p>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.</p><p><br></p><p>The treatments applied to this experiment were:</p><p>* No stress</p><p>* Nitrogen stress</p><p>* Poor potato emergence</p><p>* Phytophtora (only in 2024)</p><p>* Weeds </p><p>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.</p> ER -