Dynamic early warning prevention and control system based on forestry disease and insect pest recognition

By using drones to flip leaves through the airflow and obtain leaf back image data, combined with adaptive parameter optimization and visual feature recognition of pests and diseases, the problem of difficulty in obtaining leaf back image information in existing technologies has been solved. This enables accurate identification and early warning of pests and diseases, and improves the adaptability and data acquisition stability of the monitoring system.

CN122116204APending Publication Date: 2026-05-29BAOQING COUNTY LISHU FARM
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Patent Information

Application Number
CN202610222875.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-25
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing technologies are insufficient to obtain sufficient image information of the underside of leaves in forestry pest and disease monitoring, resulting in poor pest and disease identification and early warning effects.

Method used

By using drones to flip the blades through the downwash airflow to obtain image data of the underside of the leaves, and combining adaptive parameter optimization mechanisms and visual feature recognition of pests and diseases, the leaf stiffness index is calculated to achieve accurate identification and early warning of pest and disease characteristics in the underside of the leaves.

Benefits of technology

It enables the effective acquisition and accurate identification of pest and disease characteristics in the underside of tree canopy leaves, improving the accuracy and timeliness of forestry pest and disease monitoring and ensuring the stability and adaptability of data collection in different scenarios.

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Abstract

The invention relates to the technical field of pest and disease damage identification, in particular to a dynamic early warning prevention and control system based on forestry pest and disease damage identification, which comprises the following steps: acquiring canopy leaf attribute data of a target forest region tree species and excitation source physical characteristic parameters of an inspection unmanned aerial vehicle, and determining pneumatic pressure required for turning over canopy leaves according to the leaf attribute data; determining pneumatic excitation control parameters in combination with the excitation source physical characteristic parameters and the final parameter mapping model; the unmanned aerial vehicle is controlled to utilize downwash airflow to force the blades to turn over so as to obtain blade back image data, and self-adaptive correction is conducted on the control parameters according to the effective blade back exposure rate; performing image recognition on the leaf back image data to obtain pest and disease feature data, and calculating a leaf stiffness index through time domain analysis; generating disease and pest severity according to the disease and pest characteristic data and the leaf stiffness index, and performing graded early warning; according to the invention, leaf back image information can be effectively obtained, and early accurate identification and dynamic early warning of diseases and insect pests are realized.
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