Drone methods and educational resources for plant science and agriculture
Technological advances have made drones (UAVs) increasingly important tools for the collection of trait data in plant science. Many costs for the analysis of plant populations have dropped precipitously in recent decades, particularly for genetic sequencing. Similarly, hardware advances have made it...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1630162/full |
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| author | Travis A. Parker Burcu Celebioglu Mark Watson Paul Gepts |
| author_facet | Travis A. Parker Burcu Celebioglu Mark Watson Paul Gepts |
| author_sort | Travis A. Parker |
| collection | DOAJ |
| description | Technological advances have made drones (UAVs) increasingly important tools for the collection of trait data in plant science. Many costs for the analysis of plant populations have dropped precipitously in recent decades, particularly for genetic sequencing. Similarly, hardware advances have made it increasingly simple and practical to capture drone imagery of plant populations. However, converting this imagery into high-precision and high-throughput tabular data has become a major bottleneck in plant science. Here, we describe high-throughput phenotyping methods for the analysis of numerous plant traits based on imagery from diverse sensor types. Methods can be flexibly combined to extract data related to canopy temperature, area, height, volume, vegetation indices, and summary statistics derived from complex segmentations and classifications including using methods based on artificial intelligence (AI), computer vision, and machine learning. We then describe educational and training resources for these methods, including a web page (PlantScienceDroneMethods.github.io) and an educational YouTube channel (https://www.youtube.com/@travisparkerplantscience) with step-by-step protocols, example data, and example scripts for the whole drone data processing pipeline. These resources facilitate the extraction of high-throughput and high-precision phenomic data, removing barriers to the phenomic analysis of large plant populations. |
| format | Article |
| id | doaj-art-1aadf8121d6e4fae9fca4c752bc5715e |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-1aadf8121d6e4fae9fca4c752bc5715e2025-08-20T03:41:46ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-08-011610.3389/fpls.2025.16301621630162Drone methods and educational resources for plant science and agricultureTravis A. Parker0Burcu Celebioglu1Mark Watson2Paul Gepts3Department of Plant Sciences, University of California Davis, Davis, CA, United StatesDepartment of Plant Sciences, University of California Davis, Davis, CA, United StatesDepartment of Animal Science, University of California Davis, Davis, CA, United StatesDepartment of Plant Sciences, University of California Davis, Davis, CA, United StatesTechnological advances have made drones (UAVs) increasingly important tools for the collection of trait data in plant science. Many costs for the analysis of plant populations have dropped precipitously in recent decades, particularly for genetic sequencing. Similarly, hardware advances have made it increasingly simple and practical to capture drone imagery of plant populations. However, converting this imagery into high-precision and high-throughput tabular data has become a major bottleneck in plant science. Here, we describe high-throughput phenotyping methods for the analysis of numerous plant traits based on imagery from diverse sensor types. Methods can be flexibly combined to extract data related to canopy temperature, area, height, volume, vegetation indices, and summary statistics derived from complex segmentations and classifications including using methods based on artificial intelligence (AI), computer vision, and machine learning. We then describe educational and training resources for these methods, including a web page (PlantScienceDroneMethods.github.io) and an educational YouTube channel (https://www.youtube.com/@travisparkerplantscience) with step-by-step protocols, example data, and example scripts for the whole drone data processing pipeline. These resources facilitate the extraction of high-throughput and high-precision phenomic data, removing barriers to the phenomic analysis of large plant populations.https://www.frontiersin.org/articles/10.3389/fpls.2025.1630162/fullUAVUASQGISmultispectralthermalArtificial Intelligence (AI) |
| spellingShingle | Travis A. Parker Burcu Celebioglu Mark Watson Paul Gepts Drone methods and educational resources for plant science and agriculture Frontiers in Plant Science UAV UAS QGIS multispectral thermal Artificial Intelligence (AI) |
| title | Drone methods and educational resources for plant science and agriculture |
| title_full | Drone methods and educational resources for plant science and agriculture |
| title_fullStr | Drone methods and educational resources for plant science and agriculture |
| title_full_unstemmed | Drone methods and educational resources for plant science and agriculture |
| title_short | Drone methods and educational resources for plant science and agriculture |
| title_sort | drone methods and educational resources for plant science and agriculture |
| topic | UAV UAS QGIS multispectral thermal Artificial Intelligence (AI) |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1630162/full |
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