Optimizing UAV sprayer performance using field data and machine learning approaches
Unmanned Aerial Vehicles (UAVs) have become increasingly relevant in precision agriculture, particularly for plant protection applications. However, optimizing UAV spray performance remains a challenge due to the complex interactions between droplet size, flight parameters, and environmental conditi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525002461 |
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| _version_ | 1850128333198589952 |
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| author | Doğan Güneş Hideo Hasegawa |
| author_facet | Doğan Güneş Hideo Hasegawa |
| author_sort | Doğan Güneş |
| collection | DOAJ |
| description | Unmanned Aerial Vehicles (UAVs) have become increasingly relevant in precision agriculture, particularly for plant protection applications. However, optimizing UAV spray performance remains a challenge due to the complex interactions between droplet size, flight parameters, and environmental conditions. This study aims to enhance spray deposition efficiency by integrating field experiments, statistical evaluations, and based on physics simulation models. Experimental data were collected under actual conditions field conditions using four droplet size categories (60, 100, 140, and 180 μm) and two flight altitudes (2 m and 3 m) at speeds of 5.5 m/s and 6.9 m/s. Coverage rate, real droplet size, and deposition uniformity were assessed. Statistical tests (Levene’s test, Kruskal-Wallis, and Dunn’s test) identified significant differences among experimental groups. Machine learning models (Random Forest and XGBoost) were employed to evaluate parameter importance and predict real droplet size and coverage performance. Additionally, particle-based simulations were conducted in Python to visualize droplet deposition patterns under six UAV flight scenarios. Results showed that flight speed and droplet size were the most influential factors affecting spray coverage. Among the tested configurations, the 2 m / 5.5 m/s combination provided the best balance between coverage rate and distribution uniformity. The heatmaps based on simulation confirmed and visually supported the experimental findings. This integrated approach demonstrates the potential of combining field data with simulation and machine learning to optimize UAV spraying strategies, offering a framework for broader applications in sustainable agriculture. |
| format | Article |
| id | doaj-art-7052cf2b67c84326a9eb13a5604bf8cc |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-7052cf2b67c84326a9eb13a5604bf8cc2025-08-20T02:33:20ZengElsevierSmart Agricultural Technology2772-37552025-08-011110101310.1016/j.atech.2025.101013Optimizing UAV sprayer performance using field data and machine learning approachesDoğan Güneş0Hideo Hasegawa1Graduate School of Science and Technology, Niigata University, Niigata 950-2181, Japan; Corresponding author.Institute of Science and Technology, Niigata University, Niigata 950-2181, JapanUnmanned Aerial Vehicles (UAVs) have become increasingly relevant in precision agriculture, particularly for plant protection applications. However, optimizing UAV spray performance remains a challenge due to the complex interactions between droplet size, flight parameters, and environmental conditions. This study aims to enhance spray deposition efficiency by integrating field experiments, statistical evaluations, and based on physics simulation models. Experimental data were collected under actual conditions field conditions using four droplet size categories (60, 100, 140, and 180 μm) and two flight altitudes (2 m and 3 m) at speeds of 5.5 m/s and 6.9 m/s. Coverage rate, real droplet size, and deposition uniformity were assessed. Statistical tests (Levene’s test, Kruskal-Wallis, and Dunn’s test) identified significant differences among experimental groups. Machine learning models (Random Forest and XGBoost) were employed to evaluate parameter importance and predict real droplet size and coverage performance. Additionally, particle-based simulations were conducted in Python to visualize droplet deposition patterns under six UAV flight scenarios. Results showed that flight speed and droplet size were the most influential factors affecting spray coverage. Among the tested configurations, the 2 m / 5.5 m/s combination provided the best balance between coverage rate and distribution uniformity. The heatmaps based on simulation confirmed and visually supported the experimental findings. This integrated approach demonstrates the potential of combining field data with simulation and machine learning to optimize UAV spraying strategies, offering a framework for broader applications in sustainable agriculture.http://www.sciencedirect.com/science/article/pii/S2772375525002461Sustainable agricultureSmart farming technologiesAgricultural dronesUAV sprayersPlant protection machineryMachine learning |
| spellingShingle | Doğan Güneş Hideo Hasegawa Optimizing UAV sprayer performance using field data and machine learning approaches Smart Agricultural Technology Sustainable agriculture Smart farming technologies Agricultural drones UAV sprayers Plant protection machinery Machine learning |
| title | Optimizing UAV sprayer performance using field data and machine learning approaches |
| title_full | Optimizing UAV sprayer performance using field data and machine learning approaches |
| title_fullStr | Optimizing UAV sprayer performance using field data and machine learning approaches |
| title_full_unstemmed | Optimizing UAV sprayer performance using field data and machine learning approaches |
| title_short | Optimizing UAV sprayer performance using field data and machine learning approaches |
| title_sort | optimizing uav sprayer performance using field data and machine learning approaches |
| topic | Sustainable agriculture Smart farming technologies Agricultural drones UAV sprayers Plant protection machinery Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525002461 |
| work_keys_str_mv | AT dogangunes optimizinguavsprayerperformanceusingfielddataandmachinelearningapproaches AT hideohasegawa optimizinguavsprayerperformanceusingfielddataandmachinelearningapproaches |