AI-enabled droplet detection and tracking for agricultural spraying systems

This work leverages recent advancements in computer vision and deep learning to detect and track the motion of droplets captured by a camera. While classical computer vision techniques have been employed for detection and tracking, those approaches have limitations and are not trivially extended to droplets. We approach the problems of droplet detection and tracking through a data-driven framework, in which an annotated database of droplet images is built and object detection and tracking models are trained on this database. The accuracy of the model is evaluated and the whole process is discussed. At this point, droplet geometric properties can be extracted. This information is critical in understanding the effectiveness of a system that is spraying the droplets.

Automated summary

This study utilizes recent advancements in computer vision and deep learning to detect and track the motion of droplets. By building a database and training models, the geometric properties of the droplets can be extracted to understand the effectiveness of the spraying system.