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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.\u003cbr\u003e\u003cstrong\u003eCollection\u003c/strong\u003eThe 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. \u003cbr\u003eMetadata (MIAPPE format): https://doi.org/10.4121/20ef3f20-ec7b-4236-b13b-e4f7d54a34bf\u003cbr\u003eGithub (to load and visualise metadata):https://github.com/NPEC-NL/MIAPPE_TTADDA_dataset/tree/main\u003cbr\u003eFive studies:TTADDA_NARO_2021: https://doi.org/10.4121/f2307c47-9a1a-474a-a0d9-e09ee1b7512cTTADDA_NARO_2022: https://doi.org/10.4121/ed9b9cd6-8d69-411b-9054-1ecce543ac1bTTADDA_NARO_2023: https://doi.org/10.4121/c5f013d0-85e0-4feb-b653-a3c59683a2bcTTADDA_WUR_2022: https://doi.org/10.4121/1f628b56-3246-4aab-accd-1193b1566763TTADDA_WUR_2023: https://doi.org/10.4121/75c01fac-f00a-4980-8cd8-cd4499f1aa98 \u003cbr\u003e\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <titles>
    <title>TTADDA-UAV: A Multi-Season RGB and Multispectral UAV Dataset of Potato Fields Collected in Japan and the Netherlands</title>
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  <publicationYear>2025</publicationYear>
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    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="82">Plant Production and Plant Primary Products</subject>
    <subject>phenotyping</subject>
    <subject>yield estimation</subject>
    <subject>agriculture</subject>
    <subject>orthomosaic</subject>
    <subject>uav</subject>
    <subject>Time: 2021 - 2023</subject>
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    <description descriptionType="Abstract">&lt;p&gt;&lt;strong&gt;Background information&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Collection&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;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. &lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Metadata (MIAPPE format): &lt;/p&gt;&lt;p&gt;https://doi.org/10.4121/20ef3f20-ec7b-4236-b13b-e4f7d54a34bf&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Github (to load and visualise metadata):&lt;/p&gt;&lt;p&gt;https://github.com/NPEC-NL/MIAPPE_TTADDA_dataset/tree/main&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Five studies:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;TTADDA_NARO_2021: https://doi.org/10.4121/f2307c47-9a1a-474a-a0d9-e09ee1b7512c&lt;/li&gt;&lt;li&gt;TTADDA_NARO_2022: https://doi.org/10.4121/ed9b9cd6-8d69-411b-9054-1ecce543ac1b&lt;/li&gt;&lt;li&gt;TTADDA_NARO_2023: https://doi.org/10.4121/c5f013d0-85e0-4feb-b653-a3c59683a2bc&lt;/li&gt;&lt;li&gt;TTADDA_WUR_2022: https://doi.org/10.4121/1f628b56-3246-4aab-accd-1193b1566763&lt;/li&gt;&lt;li&gt;TTADDA_WUR_2023: https://doi.org/10.4121/75c01fac-f00a-4980-8cd8-cd4499f1aa98&lt;/li&gt;&lt;/ul&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;</description>
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