This dataset is collected from both symptomatic and non-symptomatic plants during the growing seasons of 2019 and 2022, on 40x20 m experimental fields in Lemberge (Merelbeke), Belgium (50.986544°N, 3.774066°E) using a DJI M600 PRO unmanned aerial vehicle equiped with a modified Sony Alpha 7III camera with 135 mm lens. The field trial is conducted in analogy to the method described by Van De Vijver et al. (2020, 2022), using two different cultivers, Spunta (2019) and Fontane (2022) respectively. The dataset of 2019 comprises data from three different flights (3, 6 and 9 days after inoculation) and the dataset of 2022 from four different flights (5,7, 9 and 13 days after inoculation).
This dataset consists out of 7660 patches of 256x256 pixels, cropped out of the original images, labeled and sorted in two categories (1: Alternaria, 0: no Alternaria), accompagned by a csv file containing the following information:
Original patch name
Random patch name (used during the labeling process)
Row patch number
Column patch number
Block number, column block number and row block number
Original mage name
Coordinates of original image: latitude, longitude, altitude
Date of flight
Label (0: no Alternaria, 1: Alternaria)
More detailed information about this dataset (both the collection and the preprocessing) can be found in the corresponding article 'Ultra-high-resolution UAV-Imaging and Supervised Deep Learning for Accurate Detection of Alternaria Solani in Potato Fields.'
If you use this dataset, please refer to the related journal paper as follows: "Wieme J, Leroux S, Cool SR, Van Beek J, Pieters JG and Maes WH (2024) Ultra-highresolution UAV-imaging and supervised deep learning for accurate detection of Alternaria solani in potato fields. Front. Plant Sci. 15:1206998. doi: 10.3389/fpls.2024.1206998"
This dataset was gathered within the Proeftuin Smart Farming 4.0 project (180503) within the Industry 4.0 Living Labs with funding from Flanders innovation & entrepreneurship (VLAIO, Belgium) and in the Horizon 2020 project SmartAgriHubs - Connecting the dots to unleash the innovation potential for digital transformation of the European agrifood sector with funding from the European Union under grant agreement No. 818182. Jana Wieme is funded by grant 1SE3921N of Research Foundation Flanders (FWO).
This dataset is collected from both symptomatic and non-symptomatic plants during the growing seasons of 2019 and 2022, on 40x20 m experimental fields in Lemberge (Merelbeke), Belgium (50.986544°N, 3.774066°E) using a DJI M600 PRO unmanned aerial vehicle equiped with a modified Sony Alpha 7III camera with 135 mm lens. The field trial is conducted in analogy to the method described by Van De Vijver et al. (2020, 2022), using two different cultivers, Spunta (2019) and Fontane (2022) respectively. The dataset of 2019 comprises data from three different flights (3, 6 and 9 days after inoculation) and the dataset of 2022 from four different flights (5,7, 9 and 13 days after inoculation). This dataset consists out of 7660 patches of 256x256 pixels, cropped out of the original images, labeled and sorted in two categories (1: Alternaria, 0: no Alternaria), accompagned by a csv file containing the following information: Original patch name Random patch name (used during the labeling process) Row patch number Column patch number Block number, column block number and row block number Original mage name Coordinates of original image: latitude, longitude, altitude Date of flight Label (0: no Alternaria, 1: Alternaria) More detailed information about this dataset (both the collection and the preprocessing) can be found in the corresponding article 'Ultra-high-resolution UAV-Imaging and Supervised Deep Learning for Accurate Detection of Alternaria Solani in Potato Fields.' If you use this dataset, please refer to the related journal paper as follows: "Wieme J, Leroux S, Cool SR, Van Beek J, Pieters JG and Maes WH (2024) Ultra-highresolution UAV-imaging and supervised deep learning for accurate detection of Alternaria solani in potato fields. Front. Plant Sci. 15:1206998. doi: 10.3389/fpls.2024.1206998" This dataset was gathered within the Proeftuin Smart Farming 4.0 project (180503) within the Industry 4.0 Living Labs with funding from Flanders innovation & entrepreneurship (VLAIO, Belgium) and in the Horizon 2020 project SmartAgriHubs - Connecting the dots to unleash the innovation potential for digital transformation of the European agrifood sector with funding from the European Union under grant agreement No. 818182. Jana Wieme is funded by grant 1SE3921N of Research Foundation Flanders (FWO).
Datum ter beschikking | 29-feb.-2024 |
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Uitgever | Zenodo |
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Datum van dataproductie | 1-jan.-2019 - 31-dec.-2022 |
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