The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying
Open accessAbstract
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.
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Cites
A survey on Image Data Augmentation for Deep Learning
Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203
A Survey of CPU-GPU Heterogeneous Computing Techniques
Mittal, S., & Vetter, J. S. (2015) · ACM Computing Surveys · doi:10.1145/2788396 · Cited by 568
Mazzia, V., Khaliq, A., Salvetti, F., & Chiaberge, M. (2020) · IEEE Access · doi:10.1109/access.2020.2964608 · Cited by 212
Cited by
Paymode, A. S., & Malode, V. B. (2022) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2021.12.002 · Cited by 432
A comparative study of deep learning and internet of things for precision agriculture
Saranya, T., Deisy, C., Sridevi, S., & Anbananthen, K. S. M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106034 · Cited by 232
A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture
Kalyani, Y., & Collier, R. (2021) · Sensors · doi:10.3390/s21175922 · Cited by 228
A review on object detection in unmanned aerial vehicle surveillance
Ramachandran, A., & Sangaiah, A. K. (2021) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2021.11.005 · Cited by 165
A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme
Biglari, A., & Tang, W. (2023) · Sensors · doi:10.3390/s23042131 · Cited by 86
Metadata
- Authors
- Chen, C.-J., Huang, Y.-Y., Li, Y.-S., Chen, Y.-C., Chang, C.-Y., & Huang, Y.-M.
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 21986-21997
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3126307664
- Cited by
- 192
- References
- 3
- FWCI
- 19.02
- Type
- journal article
- Language
- en
- Open access
- gold
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- Chen, C.-J., Huang, Y.-Y., Li, Y.-S., Chen, Y.-C., Chang, C.-Y., & Huang, Y.-M. (2021). Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying. IEEE Access. https://doi.org/10.1109/access.2021.3056082
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