Deep Learning-Assisted Measurement of Liquid Sheet Structure in the Atomization of Hydraulic Nozzle Spraying
The structural parameters of the liquid sheet represent a significant factor influencing the atomization performance, and its measurement is an important part of the agrochemical atomization study. Currently, the measurement predominantly relies on commercial software with manual operation, which is...
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MDPI AG
2025-02-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/2/409 |
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| author | Wenlong Yan Longlong Li Jianli Song Peng Hu Gang Xu Qiangjia Wu Ruirui Zhang Liping Chen |
| author_facet | Wenlong Yan Longlong Li Jianli Song Peng Hu Gang Xu Qiangjia Wu Ruirui Zhang Liping Chen |
| author_sort | Wenlong Yan |
| collection | DOAJ |
| description | The structural parameters of the liquid sheet represent a significant factor influencing the atomization performance, and its measurement is an important part of the agrochemical atomization study. Currently, the measurement predominantly relies on commercial software with manual operation, which is labor intensive and inefficient. In this study, deep learning methods with high-speed photographing were employed to measure the structural parameters of the liquid sheet of hydraulic nozzles with different atomization modes. The LM-YOLO liquid sheet structure recognition model was constructed to recognize the liquid sheet and perforations. Based on the recognition results, a method is designed to calculate several key parameters, including the breakup length, the liquid sheet area, the spray angle, the average number of perforations, and the average perforation area. A comparative scrutiny of the assorted liquid sheet structural parameters under different experimental conditions was also implemented. Based on the constructed model, a recognition accuracy of 81.0% for the liquid sheet structure of the LU nozzle (a classical hydraulic nozzle with high liquid sheet integrity) and 71.3% for the IDK nozzle (an air-induced hydraulic nozzle with a certain amount of bubbles in the liquid sheet) was achieved. The liquid sheet structure was measured based on the recognition results. It was found that the pressure has a significant impact on the structural parameters of the liquid film. For the LU120-03 nozzle, the breakup length of the liquid film decreases from 48.96 mm to 39.05 mm as the pressure increases. In contrast, for the IDK120-03 nozzle, the breakup length exhibits fluctuating changes, with a peak value of 29.65 mm occurring at 250 kPa. After adding silicone adjuvant, the breakup length and area of the liquid film generally decrease. The variation trends of the measured structural parameters under different experimental conditions are consistent with the trends of the data in previous relevant research by other scholars. This study provides a new method for measuring out the structural parameters of the liquid sheet, and it has potential application in related fields. |
| format | Article |
| id | doaj-art-716b368cee564c8ba61351d0957c91ea |
| institution | DOAJ |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-716b368cee564c8ba61351d0957c91ea2025-08-20T02:44:29ZengMDPI AGAgronomy2073-43952025-02-0115240910.3390/agronomy15020409Deep Learning-Assisted Measurement of Liquid Sheet Structure in the Atomization of Hydraulic Nozzle SprayingWenlong Yan0Longlong Li1Jianli Song2Peng Hu3Gang Xu4Qiangjia Wu5Ruirui Zhang6Liping Chen7School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaResearch Center for Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaCollege of Science, China Agriculture University, Beijing 100193, ChinaResearch Center for Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaResearch Center for Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaNational Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, ChinaResearch Center for Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaThe structural parameters of the liquid sheet represent a significant factor influencing the atomization performance, and its measurement is an important part of the agrochemical atomization study. Currently, the measurement predominantly relies on commercial software with manual operation, which is labor intensive and inefficient. In this study, deep learning methods with high-speed photographing were employed to measure the structural parameters of the liquid sheet of hydraulic nozzles with different atomization modes. The LM-YOLO liquid sheet structure recognition model was constructed to recognize the liquid sheet and perforations. Based on the recognition results, a method is designed to calculate several key parameters, including the breakup length, the liquid sheet area, the spray angle, the average number of perforations, and the average perforation area. A comparative scrutiny of the assorted liquid sheet structural parameters under different experimental conditions was also implemented. Based on the constructed model, a recognition accuracy of 81.0% for the liquid sheet structure of the LU nozzle (a classical hydraulic nozzle with high liquid sheet integrity) and 71.3% for the IDK nozzle (an air-induced hydraulic nozzle with a certain amount of bubbles in the liquid sheet) was achieved. The liquid sheet structure was measured based on the recognition results. It was found that the pressure has a significant impact on the structural parameters of the liquid film. For the LU120-03 nozzle, the breakup length of the liquid film decreases from 48.96 mm to 39.05 mm as the pressure increases. In contrast, for the IDK120-03 nozzle, the breakup length exhibits fluctuating changes, with a peak value of 29.65 mm occurring at 250 kPa. After adding silicone adjuvant, the breakup length and area of the liquid film generally decrease. The variation trends of the measured structural parameters under different experimental conditions are consistent with the trends of the data in previous relevant research by other scholars. This study provides a new method for measuring out the structural parameters of the liquid sheet, and it has potential application in related fields.https://www.mdpi.com/2073-4395/15/2/409atomization measurementstructural parameter measurementliquid sheet structuredeep learningnozzle spraying |
| spellingShingle | Wenlong Yan Longlong Li Jianli Song Peng Hu Gang Xu Qiangjia Wu Ruirui Zhang Liping Chen Deep Learning-Assisted Measurement of Liquid Sheet Structure in the Atomization of Hydraulic Nozzle Spraying Agronomy atomization measurement structural parameter measurement liquid sheet structure deep learning nozzle spraying |
| title | Deep Learning-Assisted Measurement of Liquid Sheet Structure in the Atomization of Hydraulic Nozzle Spraying |
| title_full | Deep Learning-Assisted Measurement of Liquid Sheet Structure in the Atomization of Hydraulic Nozzle Spraying |
| title_fullStr | Deep Learning-Assisted Measurement of Liquid Sheet Structure in the Atomization of Hydraulic Nozzle Spraying |
| title_full_unstemmed | Deep Learning-Assisted Measurement of Liquid Sheet Structure in the Atomization of Hydraulic Nozzle Spraying |
| title_short | Deep Learning-Assisted Measurement of Liquid Sheet Structure in the Atomization of Hydraulic Nozzle Spraying |
| title_sort | deep learning assisted measurement of liquid sheet structure in the atomization of hydraulic nozzle spraying |
| topic | atomization measurement structural parameter measurement liquid sheet structure deep learning nozzle spraying |
| url | https://www.mdpi.com/2073-4395/15/2/409 |
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