Document Type
Article
Publication Date
11-2022
Abstract
his work leverages recent advancements in com- puter 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.
Journal
Computers and Electronics in Agriculture
DOI
10.1016/j.compag.2022.107325
Volume
202
Issue
107325
Publisher
Elsevier
Rights
In copyright. All rights reserved.
Recommended Citation
Acharya, Praneel; Burgers, Travis A.; and Nguyen, Doang, "AI-Enabled Droplet Detection and Tracking for Agricultural Spraying Systems" (2022). Mechanical Engineering Faculty Publications. 13.
https://openprairie.sdstate.edu/me_pubs/13
Comments
This is the peer-reviewed, accepted manuscript. Posted with permission following the required embargo period.
The version of record can be found here : Computers and Electronics in Agriculture, Volume 202, 2022, 107325. https://doi.org/10.1016/j.compag.2022.107325.