AI Orchard Monitoring and UAV Spraying for Citrus Psyllid Control
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Solution Overview
Problem
Current methods for controlling citrus psyllid, the vector of citrus huanglongbing (HLB), are delayed, miss the optimal treatment time, lead to rapid psyllid population expansion, and result in environmental pollution due to widespread pesticide use.
Innovation Solution
A monitoring and emergency control system utilizing 360-degree cameras, AI image recognition, and UAVs for real-time psyllid detection and targeted pesticide application, enabling early warning and precise psyllid eradication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual investigation and widespread pesticide application are used, then psyllid control is attempted, but insect information is delayed and the best extinguishment time is missed
Solution Approach 1:
The patent replaces manual investigation with automated video acquisition modules and AI image recognition systems. Cameras continuously capture orchard images, and neural network algorithms automatically identify psyllids, eliminating the time delay inherent in manual inspection and enabling immediate detection and response.
2Reliability
If widespread pesticide application is used throughout the orchard, then psyllid population is targeted, but environmental pollution and excessive pesticide residues occur
Solution Approach 1:
The patent transitions from uniform orchard-wide pesticide application to localized targeted spraying. The UAV system delivers pesticides precisely to locations where psyllids are detected by the video and image recognition system, maintaining control effectiveness while minimizing environmental exposure and pesticide residue.
Solution Approach 2:
The system performs preliminary detection through continuous video monitoring and AI recognition before pesticide application. This advance identification of psyllid locations enables precise targeting of pesticide delivery, preventing the need for broad-spectrum application and reducing environmental harm.
3Productivity
If manual investigation methods are used, then psyllid population can be monitored, but the psyllid population expands rapidly and spreads to adjacent regions
Solution Approach 1:
The patent implements continuous monitoring through video acquisition modules that operate without interruption, coupled with real-time AI image recognition processing. This continuous detection capability eliminates the gaps inherent in periodic manual surveys, enabling immediate identification of psyllid outbreaks and rapid deployment of control measures before populations expand.
4Reliability
If conventional control methods are used, then psyllid management is attempted, but the system complexity and cost increase without improved effectiveness
Solution Approach 1:
The patent introduces an intermediary AI image recognition layer between video capture and control action. The neural network algorithm processes raw video data, automatically identifies psyllids, and generates location information for targeted spraying, simplifying the overall system architecture while dramatically improving detection accuracy compared to manual methods.
Data Source
AI summary
A monitoring and emergency control system for vector psyllid of citrus huanglongbing (HLB) is disclosed. The system includes a video acquisition module, an image recognition module, a control console, an early warning module and an unmanned aerial vehicle (UAV)-based flight prevention module. The video acquisition module acquires images by using 360-degree dead-angle-free cameras, the cameras are arranged at a plurality of points at a periphery and interior of an orchard, and each camera is numbered. The video acquisition module acquires real-time images, and transmits the real-time images to the image recognition module in real time for recognition and determination. The control console determines whether to send out warning information to an orchard manager and a flight prevention instruction according to a feedback result. The UAV-based flight prevention module receives the instruction, and then carries a pesticide box to take off to a region to kill the psyllid by applying pesticides.