Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation
Unmanned aerial spraying systems (UASSs) are widely used today for the effective control of pests affecting crops, and more advanced UASS techniques are now being developed. To evaluate such systems, artificial targets are typically used to assess droplet coverage through image processing. To evalua...
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MDPI AG
2024-11-01
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| author | Dae-Hyun Lee Baek-Gyeom Seong Seung-Yun Baek Chun-Gu Lee Yeong-Ho Kang Xiongzhe Han Seung-Hwa Yu |
| author_facet | Dae-Hyun Lee Baek-Gyeom Seong Seung-Yun Baek Chun-Gu Lee Yeong-Ho Kang Xiongzhe Han Seung-Hwa Yu |
| author_sort | Dae-Hyun Lee |
| collection | DOAJ |
| description | Unmanned aerial spraying systems (UASSs) are widely used today for the effective control of pests affecting crops, and more advanced UASS techniques are now being developed. To evaluate such systems, artificial targets are typically used to assess droplet coverage through image processing. To evaluate performance accurately, high-quality binary image processing is necessary; however, this involves labor for sample collection, transportation, and storage, as well as the risk of potential contamination during the process. Therefore, rapid assessment in the field is essential. In the present study, we evaluated droplet coverage on water-sensitive papers (WSPs) under field conditions. A dataset was constructed consisting of paired training examples, each comprising source and target data. The source data were high-quality labeled images obtained from WSP samples through image processing, while the target data were aligned RoIs within field images captured in situ. Droplet coverage estimation was performed using an encoder–decoder model, trained on the labeled images, with features adapted to field images via self-supervised learning. The results indicate that the proposed method detected droplet coverage in field images with an error of less than 5%, demonstrating a strong correlation between measured and estimated values (R<sup>2</sup> = 0.99). The method proposed in this paper enables immediate and accurate evaluation of the performance of UASSs in situ. |
| format | Article |
| id | doaj-art-ccf61cb7d2a04cb69bd071cafcc73c72 |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-ccf61cb7d2a04cb69bd071cafcc73c722025-08-20T02:07:59ZengMDPI AGDrones2504-446X2024-11-0181167010.3390/drones8110670Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive SegmentationDae-Hyun Lee0Baek-Gyeom Seong1Seung-Yun Baek2Chun-Gu Lee3Yeong-Ho Kang4Xiongzhe Han5Seung-Hwa Yu6Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Agriculture Engineering, National Institute of Agricultural Sciences, Jeonju 54875, Republic of KoreaDepartment of Crops and Food, Jeonbuk State Agricultural Research and Extension Services, Iksan 54591, Republic of KoreaDepartment of Biosystems Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Agriculture Engineering, National Institute of Agricultural Sciences, Jeonju 54875, Republic of KoreaUnmanned aerial spraying systems (UASSs) are widely used today for the effective control of pests affecting crops, and more advanced UASS techniques are now being developed. To evaluate such systems, artificial targets are typically used to assess droplet coverage through image processing. To evaluate performance accurately, high-quality binary image processing is necessary; however, this involves labor for sample collection, transportation, and storage, as well as the risk of potential contamination during the process. Therefore, rapid assessment in the field is essential. In the present study, we evaluated droplet coverage on water-sensitive papers (WSPs) under field conditions. A dataset was constructed consisting of paired training examples, each comprising source and target data. The source data were high-quality labeled images obtained from WSP samples through image processing, while the target data were aligned RoIs within field images captured in situ. Droplet coverage estimation was performed using an encoder–decoder model, trained on the labeled images, with features adapted to field images via self-supervised learning. The results indicate that the proposed method detected droplet coverage in field images with an error of less than 5%, demonstrating a strong correlation between measured and estimated values (R<sup>2</sup> = 0.99). The method proposed in this paper enables immediate and accurate evaluation of the performance of UASSs in situ.https://www.mdpi.com/2504-446X/8/11/670unmanned aerial spraying system (UASS)droplet coveragewater-sensitive paper (WSP)deep neural networksdomain adaptationself-supervised contrastive learning |
| spellingShingle | Dae-Hyun Lee Baek-Gyeom Seong Seung-Yun Baek Chun-Gu Lee Yeong-Ho Kang Xiongzhe Han Seung-Hwa Yu Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation Drones unmanned aerial spraying system (UASS) droplet coverage water-sensitive paper (WSP) deep neural networks domain adaptation self-supervised contrastive learning |
| title | Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation |
| title_full | Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation |
| title_fullStr | Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation |
| title_full_unstemmed | Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation |
| title_short | Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation |
| title_sort | coverage estimation of droplets sprayed on water sensitive papers based on domain adaptive segmentation |
| topic | unmanned aerial spraying system (UASS) droplet coverage water-sensitive paper (WSP) deep neural networks domain adaptation self-supervised contrastive learning |
| url | https://www.mdpi.com/2504-446X/8/11/670 |
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