A Deep-learning Framework for Spray Pattern Segmentation and Estimation in Agricultural Spraying Systems

Authors

Praneel Acharya, South Dakota State UniversityFollow
Travis Burgers, South Dakota State UniversityFollow
Kim-Doang Nguyen, Florida Institute of Technology

Document Type

Article

Publication Date

5-2023

Abstract

This work focuses on leveraging deep learning for agricultural applications, especially for spray pattern segmentation and spray cone angle estimation. These two characteristics are important to understanding the sprayer system such as nozzles used in agriculture. The core of this work includes three deep-learning convolution-based models. These models are trained and their performances are compared. After the best model is selected based on its performance, it is used for spray region segmentation and spray cone angle estimation. The output from the selected model provides a binary image representing the spray region. This binary image is further processed using image processing to estimate the spray cone angle. The validation process is designed to compare results obtained from this work with manual measurements.

Journal

Scientific Reports

DOI

10.1038/s41598-023-34320-7

Volume

Article number: 7545

Publisher

Nature

Rights

Copyright © 2023 the Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Acharya, P., Burgers, T. & Nguyen, KD. A deep-learning framework for spray pattern segmentation and estimation in agricultural spraying systems. Sci Rep 13, 7545 (2023). https://doi.org/10.1038/s41598-023-34320-7