Efficient Real-Time Droplet Tracking in Crop-Spraying Systems

Truong Nhut Huynh, Travis Burgers and Kim-Doang Nguyen ()
Additional contact information
Truong Nhut Huynh: Florida Institute of Technology, Melbourne, FL 32901, USA
Travis Burgers: CNH Industrial, Sioux Falls, SD 57104, USA
Kim-Doang Nguyen: Florida Institute of Technology, Melbourne, FL 32901, USA

Agriculture, 2024, vol. 14, issue 10, 1-28

Abstract: Spray systems in agriculture serve essential roles in the precision application of pesticides, fertilizers, and water, contributing to effective pest control, nutrient management, and irrigation. These systems enhance efficiency, reduce labor, and promote environmentally friendly practices by minimizing chemical waste and runoff. The efficacy of a spray is largely determined by the characteristics of its droplets, including their size and velocity. These parameters are not only pivotal in assessing spray retention, i.e., how much of the spray adheres to crops versus becoming environmental runoff, but also in understanding spray drift dynamics. This study introduces a real-time deep learning-based approach for droplet detection and tracking which significantly improves the accuracy and efficiency of measuring these droplet properties. Our methodology leverages advanced AI techniques to overcome the limitations of previous tracking frameworks, employing three novel deep learning-based tracking methods. These methods are adept at handling challenges such as droplet occlusion and varying velocities, ensuring precise tracking in real-time potentially on mobile platforms. The use of a high-speed camera operating at 2000 frames per second coupled with innovative automatic annotation tools enables the creation of a large and accurately labeled droplet dataset for training and evaluation. The core of our framework lies in the ability to track droplets across frames, associating them temporally despite changes in appearance or occlusions. We utilize metrics including Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) to quantify the tracking algorithm’s performance. Our approach is set to pave the way for innovations in agricultural spraying systems, offering a more efficient, accurate, and environmentally responsible method of applying sprays and representing a significant step toward sustainable agricultural practices.

Keywords: droplet tracking; crop-spraying systems; deep learning; real-time detection; high-speed camera; automatic annotation; data augmentation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/10/1735/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/10/1735/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:10:p:1735-:d:1490883

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().