The dataset NutriDat-HydroBarley3 contains nutrient concentration and morphpogical trait data of four genotypes of spring barley from a hydroponic experiment in greenhouse at University of Bonn in 2020 including single-nutrient deficiencies of all 14 essential mineral nutrients plus Al and Mn toxicity.

The dataset RGB-HydroBarley1 contains 11160 RGB images of 6 time points 23. Jan - 14. Feb 2020 of five genotypes of spring barley from a hydroponic experiment in greenhouse at University of Bonn including single-nutrient deficiencies of all 14 essential mineral nutrients plus Mn-toxicity. The images are annotated with genotype and fertilizer management and have been taken for a Deep Learning approach for nutrient deficiency recognition.

The dataset RGB-HydroBarley3 contains 12258 RGB images of 6 time points 22. jun - 10. jul 2020 of four genotypes of spring barley from a hydroponic experiment in greenhouse at University of Bonn including single-nutrient deficiencies of all 14 essential mineral nutrients plus Al and Mn toxicity. The images are annotated with genotype and fertilizer management and have been taken for a Deep Learning approach for nutrient deficiency recognition.

The dataset RGB-HydroWheat3 contains 11954 RGB images of 6 time points 28. Sep - 15. Oct 2020 of four genotypes of winter wheat from a hydroponic experiment in greenhouse at University of Bonn including single-nutrient deficiencies of all 14 essential mineral nutrients, Al and Mn toxicity. The images are annotated with, plant part (shoot or root), genotype and fertilizer management and have been taken for a Deep Learning approach for nutrient deficiency recognition.

The dataset RGB-HydroWheat2 contains 13397 RGB images of 5 time points 28. Aug - 11. Sep 2020 of four genotypes of winter wheat from a hydroponic experiment in greenhouse at University of Bonn including single-nutrient deficiencies of all 14 essential mineral nutrients, Al and Mn toxicity. The images are annotated with, plant part (shoot or root), genotype and fertilizer management and have been taken for a Deep Learning approach for nutrient deficiency recognition.