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TTADDA-UAV: A multi-season RGB and multispectral UAV dataset of potato fields collected in Japan and the Netherlands.

Data in brief · 23 Aug 2025 · 10.1016/j.dib.2025.112004

Abstract

The Transition to a Data-Driven Agriculture (TTADDA) project focuses on advancing the shift toward high-tech, circular agriculture. By developing cutting-edge sensor technologies and AI-driven tools, the 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. Specifically in the Netherlands, the global leader in seed potato production and Japan that produces certified seed potatoes under strict quality controls and phytosanitary regulations. A multi-season drone 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 drone imagery (RGB and multispectral), manual yield and ground coverage measurements, and 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. Using MIAPPE for this dataset ensures consistent, 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.

Plant phenotyping relevance

ジャガイモの表現型解析を目的としたUAV RGB・マルチスペクトル画像と圃場測定を含む、多季節・多地点の再利用可能なデータセットの構築・標準化が中心である。

abstractA multi-season drone dataset from five potato trials-three in Japan and two in the Netherlands was collected.
abstractPotato phenotyping is crucial for creating high-yielding, resilient, and sustainable potato crops
abstractUsing MIAPPE for this dataset ensures consistent, clear documentation of sensors, varieties, and conditions, making the data findable, reusable, and easy to integrate with other studies.

Code and data availability

The paper is a data descriptor for the TTADDA-UAV potato phenotyping dataset (RGB/multispectral orthomosaics, DSMs, yield, ground coverage, weather) publicly deposited on 4TU.ResearchData with a DOI, plus an authors' GitHub repository for loading the MIAPPE-formatted data.

Datasetpublic

The dataset is part of the following collection: Data identification number: doi.org.10.4121/936b5772–09fc–4856–983d-1f9cc2f38d15 Direct URL to data: ( https://doi.org/10.4121/936b5772-09fc-4856-983d-1f9cc2f38d15 ) The collection consist of metadata, and five related studies: TTADDA_NARO_2021, TTADDA_NARO_2022, TTADDA_NARO_2023, TTADDA_WUR_2022, TTADDA_WUR_2023 To visualise the metadata and download the dataset we recommend the following GIT: https://github.com/NPEC-NL/MIAPPE_TTADDA_dataset Related research article None 1 Value of the D

Open resource ↗10.4121/936b5772-09fc-4856-983d-1f9cc2f38d15 · lines:57-83

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