TTADDA-UAV: A Multi-Season RGB and Multispectral UAV Dataset of Potato Fields Collected in Japan and the Netherlands

DOI:
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DOI: 10.4121/936b5772-09fc-4856-983d-1f9cc2f38d15

Datacite citation style

Marrewijk, van, Bart M.; Njehia Njane, Stephen; Tsuda, Shogo; van Culemborg, Marcel; Polder, Gerrit et. al. (2025): TTADDA-UAV: A Multi-Season RGB and Multispectral UAV Dataset of Potato Fields Collected in Japan and the Netherlands. Version 2. 4TU.ResearchData. collection. https://doi.org/unavailable
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Collection

Version 2 - 2025-08-30 (latest)
Version 1 - 2025-08-30

Background information

This collection is the result of multiple studies conducted under the Transition to a Data-Driven Agriculture (TTADDA) project. The TTADDA project aims to boost productivity through a data-centric potato production system that supports circular agricultural practices. Potato phenotyping is crucial for creating high-yielding, resilient, and sustainable potato crops, which are essential in global food systems. To support this goal, a multi-season UAV dataset from five potato trials—three in Japan and two in the Netherlands was collected. Each trial field was divided into small plots, each planted with a specific cultivar to assess varietal performance. Data included UAV based imagery (RGB and multispectral), manual yield and ground coverage measurements, and on-site weather data. The combination of sensor versatility, diverse potato varieties, and varying climate and soil conditions between Japan and the Netherlands makes this dataset highly valuable and potentially reusable for a wide range of applications. Furthermore, the data adheres to the MIAPPE (Minimal Information About Plant Phenotyping Experiment) standard thus ensuring consistency and clear documentation of sensors, varieties, and conditions, making the data findable, reusable, and easy to integrate with other studies. It also supports reproducibility and automated analysis across the multi-location trials.


Collection

The collection consists of five individual studies. Every study consisted of a potato field, which was divided into smaller plots (±1.5x3m). Each plot was planted with a specific cultivar to assess and compare the performance and traits of various potato varieties. Data collection included drone imagery, manual measurements, and on-site weather data. Weekly RGB and multispectral images were processed with Agisoft Metashape to create a DEM, RGB and multispectral orthomosaics. Manual measurements covered yield and ground coverage. All data was organised and summarized using the MIAPPE format to align with the FAIR data principles.


Metadata (MIAPPE format):

doi.org/10.4121/20ef3f20-ec7b-4236-b13b-e4f7d54a34bf


Github (to load and visualise metadata):

https://github.com/NPEC-NL/MIAPPE_TTADDA_dataset/tree/main


Five studies:

  • TTADDA_NARO_2021: doi.org/10.4121/f2307c47-9a1a-474a-a0d9-e09ee1b7512c
  • TTADDA_NARO_2022: doi.org/10.4121/ed9b9cd6-8d69-411b-9054-1ecce543ac1b
  • TTADDA_NARO_2023: doi.org/10.4121/c5f013d0-85e0-4feb-b653-a3c59683a2bc
  • TTADDA_WUR_2022: doi.org/10.4121/1f628b56-3246-4aab-accd-1193b1566763
  • TTADDA_WUR_2023: doi.org/10.4121/75c01fac-f00a-4980-8cd8-cd4499f1aa98



History

  • 2025-08-30 first online, published, posted

Publisher

4TU.ResearchData

Organizations

Wageningen University and Research (WUR), Wageningen, The Netherlands; National Agriculture and Food Research Organization (NARO), Tsukuba, Japan; Solynta, Wageningen, The Netherlands

DATASETS