Repository name: USDA Ag Data Commons [ 1 ] Direct URL to Rows 1–4 Bingo rootstock data: 10.15482/USDA.ADC/26946823
USDA Ag Data Commons · 10.15482/USDA.ADC/26946823 · lines:1-53Unverified paper record
Image dataset: UAV images and ground data of one 'Bingo' mandarin and two 'Valencia' orange rootstock trials conducted in Florida.
Data in brief · 5 Dec 2024 · 10.1016/j.dib.2024.111206
Abstract
The data are aerial images and ground tree measurement data of 3 citrus rootstock trials. Developing new citrus rootstock varieties requires field trials to test to identify selections with improved horticultural performance. A bud from a scion variety is grafted onto the rootstock and grown in a nursery until the grafted plant is ready to be planted in the field, which is in about one year. Trees in the field are assessed each year by measuring height, canopy diameter in 2 dimensions, overall health, and fruit number and quality factors when the trees begin to have a significant crop (∼3 years). Data collection of each tree is done manually. The image and ground data sets are of 3 rootstock trials that includes a 3-year-old Bingo mandarin hybrid trial of 206 trees, a 6-year-old Valencia orange trial of 643 trees, and a 7-year-old Valencia orange trials of 648 trees. Data for each trial includes aerial images and ground data of height, canopy diameters, and an overall health rating. The combination of ground validated measures and aerial images make this data set useful for building AI-based aerial image data collection applications. The data will be useful for 1) visualizing the effects of different rootstock selections and varieties on scion growth, effects that may not be fully captured with single measure metrics; and 2) development of image analysis applications and segmentation algorithms that can extract data from the images that are suitable for replacing some or all the ground measures.
Plant phenotyping relevance
柑橘樹の高さ、樹冠径、健康状態を対象とする航空画像・地上測定データセットで、画像解析やセグメンテーションによる形質抽出の開発用途が明示されており、表現型取得法が中心である。
abstractThe combination of ground validated measures and aerial images make this data set useful for building AI-based aerial image data collection applications.
abstractdevelopment of image analysis applications and segmentation algorithms that can extract data from the images that are suitable for replacing some or all the ground measures.
Code and data availability
This Data in Brief article describes its own paper-specific phenotyping assets: UAV RGB images and ground-measured canopy height/width/health data for three citrus rootstock trials, publicly deposited in USDA Ag Data Commons under DOIs 10.15482/USDA.ADC/26946823 (Bingo trial) and 10.15482/USDA.ADC/26946841 (Valencia 5–
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