UAV and AI system for large insect pest detection in agricultural fields
Aerial pest detection platform combining consumer-grade drones with open-source machine learning to identify large-bodied insects such as the Colorado Potato Beetle. Designed to overcome the limitations of traditional trap-based monitoring and enable rapid survey of invasive pest frontiers.
Overview
This solution applies unmanned aerial vehicle (UAV) imaging combined with artificial intelligence (AI) to large-scale field detection of insect pests, with an initial focus on the Colorado Potato Beetle and other large-bodied insects. Traditional insect monitoring relies on pitfall traps, lure traps, and manual counting, which are labor-intensive and difficult to scale. By pairing a widely available DJI Mavic drone with open-source AI frameworks such as Google TensorFlow, the approach aims to deliver a practical, low-cost pest survey tool that can be deployed in invasive frontier areas where rapid detection is critical for quarantine and management decisions.
Technical specifications
- Aerial platform: DJI Mavic consumer-grade drone used for low-altitude image capture over agricultural fields
- Sensor approach: High-resolution RGB imagery designed to capture large-bodied insects that are visible from low altitudes
- Detection algorithm: Open-source machine learning models (Google TensorFlow or comparable frameworks) trained to recognize adult insect individuals in aerial photographs
- Target species: Colorado Potato Beetle as the primary case study, with extension potential to other large-bodied insect pests
- Workflow: Low-altitude flight to collect imagery, followed by automated AI-based image analysis to flag and count pest occurrences
- Key advantage: Uses mature, commercially available hardware and free, open-source software, making the system easy to promote, replicate, and scale
Technology readiness level
The technology is at an early-to-mid stage of development. Prior published studies have explored UAV-based insect monitoring, but no successful reports yet exist for detecting individual insects from UAV imagery due to camera resolution limits. This project is positioned to test and define the practical detection limits of UAV+AI for pest survey, beginning with field validation on Colorado Potato Beetles. The combination of proven drone hardware and established AI platforms suggests a relatively short pathway from prototype to field-deployable tool, though detection accuracy, flight protocols, and environmental robustness still require systematic validation.
About Chinese Academy of Inspection and Quarantine
The Chinese Academy of Quality Inspection and Testing—formerly the Chinese Academy of Inspection and Quarantine—is a national public research institute in Beijing focused on applied, standards-oriented research and technical support for quality and safety regulation. Its institutional model combines research units with comprehensive testing, evaluation, training, and specimen-library facilities, enabling companies and regulators to engage through validated methods, risk assessment, technical evaluation, and shared instrumentation. The academy also maintains affiliated technology enterprises and a dedicated results-conversion function that supports standards development, technology transfer, and commercialization. Research is supported through the National Natural Science Foundation of China, national key research and development programs, and funding connected to the State Administration for Market Regulation.
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