Location: Pollinator Health in Southern Crop Ecosystems Research
Project Number: 6066-21000-001-015-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 15, 2022
End Date: Aug 15, 2024
Objective:
To develop an innovative AI-driven smart system with integrated AI, IoT, UAV, and UGV techniques (the system will automatically deliver fire ant mound treatment using insecticide-free solutions).
Approach:
1) Identify the best insecticide-free solutions for efficient fire ant control in practice. It has been shown that hot water or water with detergent can be effective fire ant mound treatment. However, an in-depth investigation and specifications of the water-based solutions on fire ant removal are still very limited. There is a lack of established knowledge of water-based solutions. We will conduct comprehensive research to investigate water-based solutions for fire ant removal. The outcome of this research will provide detailed scientific guidelines to compare and evaluate different levels and settings of water-based solutions under different conditions of mounds. We will conduct a machine learning study to find out the relationship between fire ant mound size and the required quantity and frequency of water solutions to effectively eradicate the entire fire ant colony. We will also study different feasible options using water solutions in fluid and steam modes, and investigate the effectiveness of different strategies. 2) Develop a UAV system to monitor a target field (such as a pasture) and identify fire ant mounds using deep learning and remote sensing technologies. We will develop a deep learning model for accurate fire ant mound recognition. The UAV system will also communicate with an IoT network about fire ant mound location information. 3) Develop a UGV that can be activated to reach the identified mound locations and automatically execute the water-based solution to eradicate fire ant colonies. This UGV system will be equipped with a water tank, a robot-arm controlled water injection tube, and an integrated AI system to control the UGV system.
