Precision agricultural robotic sprayer with real-time Tobacco recognition and spraying system based on deep learning

Nasir, Fazal E.; Tufail, Muhammad; Haris, Muhammad; Iqbal, Jamshed; Khan, Said; Khan, Muhammad Tahir

Abstract

Precision agricultural techniques try to prevent either an excessive or inadequate application of agrochemicals during pesticide application. In recent years, it has become popular to combine traditional agricultural practices with artificial intelligence algorithms. This research presents a case study of variable-rate targeted spraying using deep learning for tobacco plant recognition and identification in a real tobacco field. An extensive comparison of the detection performance of six YOLO-based models for the tobacco crop has been performed based on experimentation in tobacco fields. An F1-score of 87.2% and a frame per second rate of 67 were achieved using the YOLOv5n model trained on actual field data. Additionally, a novel disturbance-based pressure and flow control method has been introduced to address the issue of unwanted pressure fluctuations that are typically associated with bangbang control. The quality of spray achieved by attenuation of these disturbances has been evaluated both qualitatively and quantitatively using three different spraying case studies: broadcast, and selective spraying at 20 psi pressure; and variable-rate spraying at pressure varying from 15-120 psi. As compared to the broadcast spraying, the selective and variable rate spray methods have achieved up to 60% reduction of agrochemicals.

Citation

Nasir, F. E., Tufail, M., Haris, M., Iqbal, J., Khan, S., & Khan, M. T. (2023). Precision agricultural robotic sprayer with real-time Tobacco recognition and spraying system based on deep learning. PLoS ONE, 18(3 MARCH), Article e0283801. https://doi.org/10.1371/journal.pone.0283801

• Journal Article Type: Article
• Acceptance Date: Mar 17, 2023
• Online Publication Date: Mar 31, 2023
• Publication Date: 2023-03
• Deposit Date: Apr 15, 2023
• Publicly Available Date: Apr 17, 2023
• Journal: PLoS ONE
• Print ISSN: 1932-6203
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 3 MARCH
• Article Number: e0283801
• DOI: https://doi.org/10.1371/journal.pone.0283801
• Public URL: https://hull-repository.worktribe.com/output/4260956