Rare plants detection using a YOLOv3 neural network
Rare plant species restoration (reintroduction) is one of the main biodiversity conservation activities. Reintroduced plants need constant monitoring in order to study features of their development and control the population state. To reduce the human impact on the natural habitat of plants and simp...
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| Format: | Article |
| Language: | English |
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Samara National Research University
2024-06-01
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| Series: | Компьютерная оптика |
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| Online Access: | https://www.computeroptics.ru/eng/KO/Annot/KO48-3/480310e.html |
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| author | L.A. Gorodetskaya A.Y. Denisova L.M. Kavelenova V.A. Fedoseev |
| author_facet | L.A. Gorodetskaya A.Y. Denisova L.M. Kavelenova V.A. Fedoseev |
| author_sort | L.A. Gorodetskaya |
| collection | DOAJ |
| description | Rare plant species restoration (reintroduction) is one of the main biodiversity conservation activities. Reintroduced plants need constant monitoring in order to study features of their development and control the population state. To reduce the human impact on the natural habitat of plants and simplify the monitoring process, we propose the use of automatic analysis of unmanned aerial vehicles (UAVs) data using the Yolov3 neural network. The article discusses neural network parameters for detecting Paeonia Tenuifolia, reintroduced in the Samara region by ecologists from the Department of Ecology, Botany and Nature Conservation of Samara University. The main issue under research is the possibility of training a neural network from peony images collected in an artificial habitat with a subsequent application to images collected in a natural habitat and the possibilities of using multi-temporal data to improve the network training quality. The experiments have shown that training a neural network exclusively using images collected in the natural habitat makes it possible to achieve a probability of correct detection of peonies of 0.93, while using data obtained at different years allows increasing the probability of correct detection to 0.95. |
| format | Article |
| id | doaj-art-029ffcc87f2c40e0938f1558a093c4cd |
| institution | Kabale University |
| issn | 0134-2452 2412-6179 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj-art-029ffcc87f2c40e0938f1558a093c4cd2025-02-06T12:13:41ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792024-06-0148339740510.18287/2412-6179-CO-1405Rare plants detection using a YOLOv3 neural networkL.A. Gorodetskaya0A.Y. Denisova1L.M. Kavelenova2V.A. Fedoseev3Samara National Research UniversitySamara National Research UniversitySamara National Research UniversitySamara National Research UniversityRare plant species restoration (reintroduction) is one of the main biodiversity conservation activities. Reintroduced plants need constant monitoring in order to study features of their development and control the population state. To reduce the human impact on the natural habitat of plants and simplify the monitoring process, we propose the use of automatic analysis of unmanned aerial vehicles (UAVs) data using the Yolov3 neural network. The article discusses neural network parameters for detecting Paeonia Tenuifolia, reintroduced in the Samara region by ecologists from the Department of Ecology, Botany and Nature Conservation of Samara University. The main issue under research is the possibility of training a neural network from peony images collected in an artificial habitat with a subsequent application to images collected in a natural habitat and the possibilities of using multi-temporal data to improve the network training quality. The experiments have shown that training a neural network exclusively using images collected in the natural habitat makes it possible to achieve a probability of correct detection of peonies of 0.93, while using data obtained at different years allows increasing the probability of correct detection to 0.95.https://www.computeroptics.ru/eng/KO/Annot/KO48-3/480310e.htmlreintroductionbiodiversityuav dataneural networksyolov3 |
| spellingShingle | L.A. Gorodetskaya A.Y. Denisova L.M. Kavelenova V.A. Fedoseev Rare plants detection using a YOLOv3 neural network Компьютерная оптика reintroduction biodiversity uav data neural networks yolov3 |
| title | Rare plants detection using a YOLOv3 neural network |
| title_full | Rare plants detection using a YOLOv3 neural network |
| title_fullStr | Rare plants detection using a YOLOv3 neural network |
| title_full_unstemmed | Rare plants detection using a YOLOv3 neural network |
| title_short | Rare plants detection using a YOLOv3 neural network |
| title_sort | rare plants detection using a yolov3 neural network |
| topic | reintroduction biodiversity uav data neural networks yolov3 |
| url | https://www.computeroptics.ru/eng/KO/Annot/KO48-3/480310e.html |
| work_keys_str_mv | AT lagorodetskaya rareplantsdetectionusingayolov3neuralnetwork AT aydenisova rareplantsdetectionusingayolov3neuralnetwork AT lmkavelenova rareplantsdetectionusingayolov3neuralnetwork AT vafedoseev rareplantsdetectionusingayolov3neuralnetwork |