Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying
Tessaratoma papillosa (Drury) first invaded Taiwan in 2009. Every year, T. papillosa causes severe damage to the longan crops. Novel applications for edge intelligence are applied in this study to establish an intelligent pest recognition system to manage this pest problem. We used a detecting drone...
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9343827/ |
| _version_ | 1818725455705931776 |
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| author | Ching-Ju Chen Ya-Yu Huang Yuan-Shuo Li Ying-Cheng Chen Chuan-Yu Chang Yueh-Min Huang |
| author_facet | Ching-Ju Chen Ya-Yu Huang Yuan-Shuo Li Ying-Cheng Chen Chuan-Yu Chang Yueh-Min Huang |
| author_sort | Ching-Ju Chen |
| collection | DOAJ |
| description | Tessaratoma papillosa (Drury) first invaded Taiwan in 2009. Every year, T. papillosa causes severe damage to the longan crops. Novel applications for edge intelligence are applied in this study to establish an intelligent pest recognition system to manage this pest problem. We used a detecting drone to photograph the pest and employed a Tiny-YOLOv3 neural network model built on an embedded system NVIDIA Jetson TX2 to recognize T. papillosa in the orchard to determine the position of the pests in real-time. The pests' positions are then used to plan the optimal pesticide spraying route for the agricultural drone. Apart from planning the optimized spraying of pesticide for the spraying drone, the TX2 embedded platform also transmits the position and generation of pests to the cloud to record and analyze the growth of longan with a computer or mobile device. This study enables farmers to understand the pest distribution and take appropriate precautions in real-time. The agricultural drone sprays pesticides only where needed, which reduces pesticide use, decreases damage to the environment, and increases crop yield. |
| first_indexed | 2024-12-17T21:42:35Z |
| format | Article |
| id | doaj.art-b7fb23864b084bf5a83e5c8b55eab5d9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T21:42:35Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b7fb23864b084bf5a83e5c8b55eab5d92022-12-21T21:31:34ZengIEEEIEEE Access2169-35362021-01-019219862199710.1109/ACCESS.2021.30560829343827Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide SprayingChing-Ju Chen0https://orcid.org/0000-0002-7092-6226Ya-Yu Huang1https://orcid.org/0000-0002-1830-7989Yuan-Shuo Li2https://orcid.org/0000-0001-7973-8646Ying-Cheng Chen3Chuan-Yu Chang4https://orcid.org/0000-0001-9476-8130Yueh-Min Huang5https://orcid.org/0000-0001-7052-1272Department of Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Yunlin, TaiwanDepartment of Engineering Science, National Cheng Kung University, Tainan, TaiwanDepartment of Engineering Science, National Cheng Kung University, Tainan, TaiwanDivision of Crop Environment, Tainan District Agricultural Research and Extension Station, Tainan, TaiwanDepartment of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, TaiwanDepartment of Engineering Science, National Cheng Kung University, Tainan, TaiwanTessaratoma papillosa (Drury) first invaded Taiwan in 2009. Every year, T. papillosa causes severe damage to the longan crops. Novel applications for edge intelligence are applied in this study to establish an intelligent pest recognition system to manage this pest problem. We used a detecting drone to photograph the pest and employed a Tiny-YOLOv3 neural network model built on an embedded system NVIDIA Jetson TX2 to recognize T. papillosa in the orchard to determine the position of the pests in real-time. The pests' positions are then used to plan the optimal pesticide spraying route for the agricultural drone. Apart from planning the optimized spraying of pesticide for the spraying drone, the TX2 embedded platform also transmits the position and generation of pests to the cloud to record and analyze the growth of longan with a computer or mobile device. This study enables farmers to understand the pest distribution and take appropriate precautions in real-time. The agricultural drone sprays pesticides only where needed, which reduces pesticide use, decreases damage to the environment, and increases crop yield.https://ieeexplore.ieee.org/document/9343827/Edge intelligenceunmanned aerial vehicles (UAV)real-time embedded systemsslope land orchardobject detectionagricultural pests damage |
| spellingShingle | Ching-Ju Chen Ya-Yu Huang Yuan-Shuo Li Ying-Cheng Chen Chuan-Yu Chang Yueh-Min Huang Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying IEEE Access Edge intelligence unmanned aerial vehicles (UAV) real-time embedded systems slope land orchard object detection agricultural pests damage |
| title | Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying |
| title_full | Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying |
| title_fullStr | Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying |
| title_full_unstemmed | Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying |
| title_short | Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying |
| title_sort | identification of fruit tree pests with deep learning on embedded drone to achieve accurate pesticide spraying |
| topic | Edge intelligence unmanned aerial vehicles (UAV) real-time embedded systems slope land orchard object detection agricultural pests damage |
| url | https://ieeexplore.ieee.org/document/9343827/ |
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