Dataset of aerial photographs acquired with UAV using a multispectral (green, red and near-infrared) camera for cherry tomato (Solanum lycopersicum var. cerasiforme) monitoring
A dataset of aerial photographs acquired with an Unmanned Aerial Vehicle (UAV) DJI Phantom 4 Pro is presented for monitoring a cherry tomato (<i>Solanum lycopersicum</i> var. <i>cerasiforme</i>) crop in Navolato, Mexico. Seven photogrammetric flights were carried out to assess the plant growth using a Mapir Survey 3W multispectral camera. Multispectral images with an approximate spatial resolution of 1.83 cm/px were obtained in each photogrammetric flight. These images were acquired every 15 days starting on October 15, 2021, and ending on January 23, 2022. The dataset contains the radiometrically calibrated images of the tomato crop divided into 2 open field parcels. The dataset also includes the processed photogrammetric products (ortho-mosaics) using a binary mask to exclude the soil from the plant area. The dataset was originally acquired to assess plant growth, stress levels, and overall crop health. However, this multispectral imagery dataset can also have various uses, such as creating training datasets with accurate labels or classes which can then be used to develop, train, and/or validate machine learning algorithms for image classification, object detection tasks, or change detection analysis.
Authors
- Antonio J. Sanhouse-García (ORCID: https://orcid.org/0000-0002-7109-4310)
- Jesús Gabriel Rangel‐Peraza (ORCID: https://orcid.org/0000-0003-0764-0185)
- Sergio Alberto Monjardín-Armenta (ORCID: https://orcid.org/0000-0002-4890-6798)
- Osiris Chávez-Martínez (ORCID: https://orcid.org/0009-0001-8577-7454)
- Zuriel Dathan Mora-Felix
Institutions
- Universidad Autónoma de Sinaloa (MX)
- Instituto Tecnológico de Culiacán (MX)
- Consejo Nacional de Ciencia y Tecnología (PY)
Publication Details
- Journal
- Data in Brief
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1016/j.dib.2024.111256
- Citations
- 3
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.38