# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 4 Discussion
### Future Work  
Because plant maturity was found to have an impact on classification accuracy, it may be advisable to collect data of each plant variety throughout the growing season and at multiple locations to help further generalize the model. Future research will likely focus on improved hardware and software integration, which will enable synchronous execution of all system tasks: hydroacoustic data collection, weed classification and distribution mapping, and targeted herbicide treatment.  
Figure 22: Deep neural networks (DNN) weight visualization for first convolutional layer (**a**) before parameter optimization and (**b**) after training with parameter optimization.  
The integrated process would involve automated weed classification during hydroacoustic scanning, which could be accomplished through one of several methods. The first option involves performing all classification and location extraction tasks on a central computer, located with the operator. In this option, the hydroacoustic data could be transferred to the central computer using existing hydroacoustic wireless technology (e.g., the \"Navico gofree wifi1\"), a wireless module (Navico Marine Electronics, Egersund, Norway), or a Wi-Fi enabled SD card. To overcome limited transmission range, transects could be configured to periodically direct boats close to the operator for wireless data transmission. Following weed classification and target location identification by the computer, an operator could generate transects covering these locations and transmit them to the vehicles for herbicide application. Multiple boats could be employed simultaneously for weed detection and treatment with a single operator (based on the shore or in another watercraft, (e.g., canoe or motorized craft)).  
The second option involves the use of onboard computer processing systems (e.g., Raspberry Pi units or tablets), for real-time image classification. Live data could be transferred from the terminals of the fish finder to the processor using Wireshark software [32]. The herbicide application system could be triggered immediately upon detection of targeted weed species. Applying this methodology, scanning and treatment could be completed in a single completion of the transects. In spite of its benefits, this method would require extensive research to successfully configure real-time communication between software (Wireshark, Reefmaster, MATLAB, Mission Planner) and hardware components (hydroacoustic scanner, autopilot module, etc.).  
Conceptualization, G.B., S.F. and R.R.; methodology: G.B., S.J., S.F. and M.P.; software, S.J. and M.P.; validation, S.J. and M.P.; data collection and processing, R.R., M.P. and S.J.; writing--original draft preparation, S.J. and M.P.; writing--review and editing, G.B. and M.P.; visualization, supervision, G.B., S.F. and R.R.; project administration, G.B. and S.F.; funding acquisition, R.R. and G.B.  
This research was funded by United Phosphorous, Inc. and the North Carolina Policy Collaboratory, grant numbers 2016-1503 and 2019-0236, respectively.  
The authors would like to acknowledge the generous technical support of [PERSON] and [PERSON] (Dept. of Crop and Soil Sciences, N.C. State University), [PERSON] and [PERSON] (United Phosphorus, Inc.), and [PERSON], [PERSON] and [PERSON] (Dept. of Mechanical and Aerospace Engineering, N.C. State University).  
The authors declare no conflicts of interest.