Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning
In this study, we show a new way for a small unmanned aerial vehicle (UAV) to move around on its own in the plantations of the tree using a single camera only. To avoid running into trees, a control plan was put into place. The detection model looks at the image heights of the trees it finds to figu...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-07-01
|
| Series: | Nonlinear Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nleng-2022-0299 |
| _version_ | 1797773454927200256 |
|---|---|
| author | Xiao Shuiqing |
| author_facet | Xiao Shuiqing |
| author_sort | Xiao Shuiqing |
| collection | DOAJ |
| description | In this study, we show a new way for a small unmanned aerial vehicle (UAV) to move around on its own in the plantations of the tree using a single camera only. To avoid running into trees, a control plan was put into place. The detection model looks at the image heights of the trees it finds to figure out how far away they are from the UAV. It then looks at the widths of the image between the trees without any obstacles to finding the largest space. The purpose of this research is to investigate how virtual reality (VR) may improve student engagement and outcomes in the classroom. The emotional consequences of virtual reality on learning, such as motivation and enjoyment, are also explored, making this fascinating research. To investigate virtual reality’s potential as a creative and immersive tool for boosting educational experiences, the study adopts a controlled experimental method. This study’s most significant contributions are the empirical evidence it provides for the efficacy of virtual reality in education, the illumination of the impact VR has on various aspects of learning, and the recommendations it offers to educators on how to make the most of VR in the classroom. |
| first_indexed | 2024-03-12T22:06:45Z |
| format | Article |
| id | doaj.art-134402b5722340dc9f6b89bfcd6deaf4 |
| institution | Directory Open Access Journal |
| issn | 2192-8029 |
| language | English |
| last_indexed | 2024-03-12T22:06:45Z |
| publishDate | 2023-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nonlinear Engineering |
| spelling | doaj.art-134402b5722340dc9f6b89bfcd6deaf42023-07-24T11:19:11ZengDe GruyterNonlinear Engineering2192-80292023-07-0112154010.1515/nleng-2022-0299Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learningXiao Shuiqing0Institute of Electromechanical Engineering, Lingnan Normal University, Zhanjiang, Guangdong 524048, ChinaIn this study, we show a new way for a small unmanned aerial vehicle (UAV) to move around on its own in the plantations of the tree using a single camera only. To avoid running into trees, a control plan was put into place. The detection model looks at the image heights of the trees it finds to figure out how far away they are from the UAV. It then looks at the widths of the image between the trees without any obstacles to finding the largest space. The purpose of this research is to investigate how virtual reality (VR) may improve student engagement and outcomes in the classroom. The emotional consequences of virtual reality on learning, such as motivation and enjoyment, are also explored, making this fascinating research. To investigate virtual reality’s potential as a creative and immersive tool for boosting educational experiences, the study adopts a controlled experimental method. This study’s most significant contributions are the empirical evidence it provides for the efficacy of virtual reality in education, the illumination of the impact VR has on various aspects of learning, and the recommendations it offers to educators on how to make the most of VR in the classroom.https://doi.org/10.1515/nleng-2022-0299convolutional neural networkuavobstacle avoidance |
| spellingShingle | Xiao Shuiqing Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning Nonlinear Engineering convolutional neural network uav obstacle avoidance |
| title | Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning |
| title_full | Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning |
| title_fullStr | Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning |
| title_full_unstemmed | Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning |
| title_short | Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning |
| title_sort | convolutional neural network for uav image processing and navigation in tree plantations based on deep learning |
| topic | convolutional neural network uav obstacle avoidance |
| url | https://doi.org/10.1515/nleng-2022-0299 |
| work_keys_str_mv | AT xiaoshuiqing convolutionalneuralnetworkforuavimageprocessingandnavigationintreeplantationsbasedondeeplearning |