Location: Stored Product Insect and Engineering Research
Project Number: 3020-43440-010-010-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 15, 2022
End Date: Sep 14, 2024
Objective:
US grain farmers grow hundreds of millions of bushels of new corn, soybeans, wheat, sorghum, rice, and other grains which are harvested over brief periods and then stored over the following year or two. Integrated pest management methods are used to maintain and protect grain and products in storage and processing facilities. One pest management method used in facilities is aerosol insecticides. Aerosol insecticides can be deployed through built in spray systems or by a certified applicator using a pressurized cylinder or mechanical fogger. Some structures such as flour mills and other processing facilities are complex in their physical structure creating challenges for manual areorsol applications. The first objective would develop robotic systems which could be used to apply the aerosols in a facility, explore methods of monitoring aerosol application coverage using imaging or LIDAR as a sensing method, and facility mapping using LIDAR or similar technology to create a robot that can define a floor plan and create a facility specific spray plan to maximize efficacy. Secondly, stored-grain insects are difficult to detect and monitor in large facilities. The robotic platform designed with so monitoring techniques such as imaging can be adapted to the platform. Artificial Intelligence and imaging methods are concurrently being developed through another NACA with Kansas State University for insect indentification.
Approach:
For the first objective, a robotic spray system would be developed which would safely hold a typical high-pressure cylindrized pesticide or similar mechanical fogger and allow remote control and manuvering of the robot and cylinder throughout a facility and to remotely activate the spray control values to apply the aerosol within the label rate, and to record and document spray location coordinate data within the facility, record treatment times during applications, and summarize the spray applications for the entire facility.
For the second objective, a robotic system would be developed to manuver throughout a defined path in a facility and sample with video and vacuum sampling of the facility floor. Monitoring for insects could be programmed to operate at prescribed frequencies such as every 24 hours or evenings when workers are not present. Images will be used as AI supplementary training data for another project with Kansas State University on insect indentification.
