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Seedling and juvenile tree detection using UAV photogrammetry and machine learning
Thesis (MSc)--Stellenbosch University, 2024.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2024
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| _version_ | 1867613903321563137 |
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| access_status_str | Open Access |
| author | Kok, Simeon James |
| author2 | Talbot, Bruce |
| author_browse | Kok, Simeon James Talbot, Bruce |
| author_facet | Talbot, Bruce Kok, Simeon James |
| author_sort | Kok, Simeon James |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch University, 2024. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/130799 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:43:32.400Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/130799 Seedling and juvenile tree detection using UAV photogrammetry and machine learning Kok, Simeon James Talbot, Bruce Ackerman, Simon Stellenbosch University. Faculty of AgriSciences. Dept. of Forest and Wood Science. UAV Object Detection Seedling Aerial photography in agriculture Machine learning Seedlings Drone aircraft UCTD Thesis (MSc)--Stellenbosch University, 2024. ENGLISH ABSTRACT: Accurate counts of seedling survival are essential for establishing a measure of mortality rates in replanted sites. This in turn measure the effectiveness of the planting operation deployed in the stand. To further this measure, advances in digital aerial photogrammetry obtained from UAVs and machine learning algorithms (object detection models) have led to increased use of this technology in the forestry industry. Seedlings and juvenile trees are ideal objects to be detected and counted by object detection models, due to their shape and contrast in colour to background in most digital orthomosaics. In this study, three different types of object detections models were trained using annotated seedlings as its training data, to detect and count the number of seedlings in 4 replanted sites. A total of 12 models were trained, 4 of each model type (SSD, Faster R-CNN and YOLOv5). The detection rates for each model were assessed and compared to eye count data as well as data measured in the field. The orthomosaics of four different sites were used to detect the seedlings located within the site. The resulting bounding box that covers a seedling was used to determine how crown diameter (seedling size) influences detection rates. The objects causing false detections (false positives) was counted to determine which objects are most likely to cause confusion for the models. The influence of data set size on detection was also studied. The results show that seedlings can be detected with high rates of accuracy with the three types of object detection models used. The best performing model being the SSD model achieving an average recall and precision of 93% and 100% on the orthomosaics of 4 different sites. Crown diameter showed to influence detection rates, as an increase in crown diameter showed an increase in detection rates. Vegetation surrounding the seedlings, such as weeds, was the main cause for false detections for the models. The number of annotations influenced model detection rates, with more annotated seedlings increasing model performance. AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar. Masters 2024-03-07T13:10:15Z 2024-05-08T11:03:26Z 2024-03-07T13:10:15Z 2024-05-08T11:03:26Z 2024-03 Thesis https://scholar.sun.ac.za/handle/10019.1/130799 en_ZA Stellenbosch University vii, 80 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | UAV Object Detection Seedling Aerial photography in agriculture Machine learning Seedlings Drone aircraft UCTD Kok, Simeon James Seedling and juvenile tree detection using UAV photogrammetry and machine learning |
| title | Seedling and juvenile tree detection using UAV photogrammetry and machine learning |
| title_full | Seedling and juvenile tree detection using UAV photogrammetry and machine learning |
| title_fullStr | Seedling and juvenile tree detection using UAV photogrammetry and machine learning |
| title_full_unstemmed | Seedling and juvenile tree detection using UAV photogrammetry and machine learning |
| title_short | Seedling and juvenile tree detection using UAV photogrammetry and machine learning |
| title_sort | seedling and juvenile tree detection using uav photogrammetry and machine learning |
| topic | UAV Object Detection Seedling Aerial photography in agriculture Machine learning Seedlings Drone aircraft UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/130799 |
| work_keys_str_mv | AT koksimeonjames seedlingandjuveniletreedetectionusinguavphotogrammetryandmachinelearning |