Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation

• Published in: Drones (Basel) 2024-11, Vol.8 (11), p.670
• Main Authors: Lee, Dae-Hyun, Seong, Baek-Gyeom, Baek, Seung-Yun, Lee, Chun-Gu, Kang, Yeong-Ho, Han, Xiongzhe, Yu, Seung-Hwa
• Format: Article
• Language: English
• Subjects: Adaptation; Adaptive control; Adaptive systems; Assessments; Crop dusting; Deep learning; deep neural networks; domain adaptation; droplet coverage; Droplets; Equipment and supplies; Error analysis; Error detection; Image processing; Image quality; Image segmentation; Performance evaluation; Pest control; Pesticides; Pests; self-supervised contrastive learning; Self-supervised learning; Smartphones; Software; Spraying and dusting in agriculture; Systems analysis; unmanned aerial spraying system (UASS); Unmanned aerial vehicles; water-sensitive paper (WSP)
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones8110670

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 (R2 = 0.99). The method proposed in this paper enables immediate and accurate evaluation of the performance of UASSs in situ.