Spray Drift Segmentation for Intelligent Spraying System Using 3D Point Cloud Deep Learning Framework
This study proposes a novel spray drift analysis method, based on 3D deep learning, managing and reducing spray drift using a mobile LiDAR method. LiDAR point clouds were trained to classify and segment spraying forms from orchards using the PointNet++ model, which is a 3D deep...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9832619/ |
| Summary: | This study proposes a novel spray drift analysis method, based on 3D deep learning, managing and reducing spray drift using a mobile LiDAR method. LiDAR point clouds were trained to classify and segment spraying forms from orchards using the PointNet++ model, which is a 3D deep learning structure. The trained deep learning model represented an accuracy of 96.23%. The spray drift analysis system was demonstrated through its application in intelligent spraying systems. Three control field experiments were performed in a pear orchard to verify the effectiveness of the system. The obtained results confirm the satisfactory performance of 3D deep learning-based spray drift analysis method. It is expected that the proposed system can measure and manage spray drift. |
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| ISSN: | 2169-3536 |