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This dataset contains all pictures used for the publication "Applying deep neural networks to predict incidence and phenology of plant pests and diseases" published in Ecosphere. We included three main folders: The folders "standardized_pictures" and "field_pictures" contain the full dataset with all pictures taken during the sampling for the study. The folder "categorized" contains pictures of the six main categories of damages which were used to train deep neural networks. Leaves for standardized pictures and pictures in the field were sampled weekly between April 15th and August 28th 2019 in three apple orchards in central Switzerland, in Kleinwangen, Gelfingen and Waedenswil (split into Weadenswil and Gottshalde in the dataset). We used cameras of two different smartphones, an IPhone 6 (8 megapixel camera) and a Sony Xperia X (23 megapixel camera). Detailed methods can be found in the publication. Codes can be found under: https://doi.org/10.5281/zenodo.5497319
Imagedata, deep learning, apple leaves, damages, pests, pathogens
Imagedata, deep learning, apple leaves, damages, pests, pathogens
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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