Next Article in Journal
A Simple Convolutional Neural Network with Rule Extraction
Next Article in Special Issue
FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection
Previous Article in Journal
A Review of Tunable Orbital Angular Momentum Modes in Fiber: Principle and Generation
Previous Article in Special Issue
A YOLOv2 Convolutional Neural Network-Based Human–Machine Interface for the Control of Assistive Robotic Manipulators
Open AccessArticle

Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species

1
Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695, USA
2
Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(12), 2410; https://doi.org/10.3390/app9122410
Received: 29 April 2019 / Revised: 5 June 2019 / Accepted: 6 June 2019 / Published: 13 June 2019
Invasive aquatic plant species can expand rapidly throughout water bodies and cause severely adverse economic and ecological impacts. While mechanical, chemical, and biological methods exist for the identification and treatment of these invasive species, they are manually intensive, inefficient, costly, and can cause collateral ecological damage. To address current deficiencies in aquatic weed management, this paper details the development of a small fleet of fully autonomous boats capable of subsurface hydroacoustic imaging (to scan aquatic vegetation), machine learning (for automated weed identification), and herbicide deployment (for vegetation control). These capabilities aim to minimize manual labor and provide more efficient, safe (reduced chemical exposure to personnel), and timely weed management. Geotagged hydroacoustic imagery of three aquatic plant varieties (Hydrilla, Cabomba, and Coontail) was collected and used to create a software pipeline for subsurface aquatic weed classification and distribution mapping. Employing deep learning, the novel software achieved a classification accuracy of 99.06% after training. View Full-Text
Keywords: autonomous vehicles; robotics; machine learning; deep learning; image preprocessing; hydroacoustic sensing autonomous vehicles; robotics; machine learning; deep learning; image preprocessing; hydroacoustic sensing
Show Figures

Figure 1

MDPI and ACS Style

Patel, M.; Jernigan, S.; Richardson, R.; Ferguson, S.; Buckner, G. Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species. Appl. Sci. 2019, 9, 2410.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop