Real-Time Droplet Detection for Agricultural Spraying Systems: A Deep Learning Approach
Nozzles are ubiquitous in agriculture: they are used to spray and apply nutrients and pesticides to crops. The properties of droplets sprayed from nozzles are vital factors that determine the effectiveness of the spray. Droplet size and other characteristics affect spray retention and drift, which i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Machine Learning and Knowledge Extraction |
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| Online Access: | https://www.mdpi.com/2504-4990/6/1/14 |
| _version_ | 1797240215851499520 |
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| author | Nhut Huynh Kim-Doang Nguyen |
| author_facet | Nhut Huynh Kim-Doang Nguyen |
| author_sort | Nhut Huynh |
| collection | DOAJ |
| description | Nozzles are ubiquitous in agriculture: they are used to spray and apply nutrients and pesticides to crops. The properties of droplets sprayed from nozzles are vital factors that determine the effectiveness of the spray. Droplet size and other characteristics affect spray retention and drift, which indicates how much of the spray adheres to the crop and how much becomes chemical runoff that pollutes the environment. There is a critical need to measure these droplet properties to improve the performance of crop spraying systems. This paper establishes a deep learning methodology to detect droplets moving across a camera frame to measure their size. This framework is compatible with embedded systems that have limited onboard resources and can operate in real time. The method leverages a combination of techniques including resizing, normalization, pruning, detection head, unified feature map extraction via a feature pyramid network, non-maximum suppression, and optimization-based training. The approach is designed with the capability of detecting droplets of various sizes, shapes, and orientations. The experimental results demonstrate that the model designed in this study, coupled with the right combination of dataset and augmentation, achieved a 97% precision and 96.8% recall in droplet detection. The proposed methodology outperformed previous models, marking a significant advancement in droplet detection for precision agriculture applications. |
| first_indexed | 2024-04-24T18:03:54Z |
| format | Article |
| id | doaj.art-6dff42d918ea4f6397081e9402996130 |
| institution | Directory Open Access Journal |
| issn | 2504-4990 |
| language | English |
| last_indexed | 2024-04-24T18:03:54Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj.art-6dff42d918ea4f6397081e94029961302024-03-27T13:52:04ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902024-01-016125928210.3390/make6010014Real-Time Droplet Detection for Agricultural Spraying Systems: A Deep Learning ApproachNhut Huynh0Kim-Doang Nguyen1Department of Mechanical and Civil Engineering, Florida Institute of Technology, Melbourne, FL 32901, USADepartment of Mechanical and Civil Engineering, Florida Institute of Technology, Melbourne, FL 32901, USANozzles are ubiquitous in agriculture: they are used to spray and apply nutrients and pesticides to crops. The properties of droplets sprayed from nozzles are vital factors that determine the effectiveness of the spray. Droplet size and other characteristics affect spray retention and drift, which indicates how much of the spray adheres to the crop and how much becomes chemical runoff that pollutes the environment. There is a critical need to measure these droplet properties to improve the performance of crop spraying systems. This paper establishes a deep learning methodology to detect droplets moving across a camera frame to measure their size. This framework is compatible with embedded systems that have limited onboard resources and can operate in real time. The method leverages a combination of techniques including resizing, normalization, pruning, detection head, unified feature map extraction via a feature pyramid network, non-maximum suppression, and optimization-based training. The approach is designed with the capability of detecting droplets of various sizes, shapes, and orientations. The experimental results demonstrate that the model designed in this study, coupled with the right combination of dataset and augmentation, achieved a 97% precision and 96.8% recall in droplet detection. The proposed methodology outperformed previous models, marking a significant advancement in droplet detection for precision agriculture applications.https://www.mdpi.com/2504-4990/6/1/14agricultural nozzlesdroplet propertiesdeep learningreal-time detectionmobile platformYOLOv8 |
| spellingShingle | Nhut Huynh Kim-Doang Nguyen Real-Time Droplet Detection for Agricultural Spraying Systems: A Deep Learning Approach Machine Learning and Knowledge Extraction agricultural nozzles droplet properties deep learning real-time detection mobile platform YOLOv8 |
| title | Real-Time Droplet Detection for Agricultural Spraying Systems: A Deep Learning Approach |
| title_full | Real-Time Droplet Detection for Agricultural Spraying Systems: A Deep Learning Approach |
| title_fullStr | Real-Time Droplet Detection for Agricultural Spraying Systems: A Deep Learning Approach |
| title_full_unstemmed | Real-Time Droplet Detection for Agricultural Spraying Systems: A Deep Learning Approach |
| title_short | Real-Time Droplet Detection for Agricultural Spraying Systems: A Deep Learning Approach |
| title_sort | real time droplet detection for agricultural spraying systems a deep learning approach |
| topic | agricultural nozzles droplet properties deep learning real-time detection mobile platform YOLOv8 |
| url | https://www.mdpi.com/2504-4990/6/1/14 |
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