AI-driven accurate prevention and control method for peach fruit moths in different growth periods

By constructing a multi-dimensional feature library and AI decision-making model, and combining visual recognition and sensor data, precise control of peach fruit moth in the Jiangsu and Zhejiang regions has been achieved. This solves the problems of poor control targeting and extensive pesticide application in existing technologies, and improves the control effect and the safety of pesticide use.

CN122074328APending Publication Date: 2026-05-26NINGBO YINZHOU VOCATIONAL SENIOR HIGH SCHOOL
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Patent Information

Application Number
CN202610191739.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-10
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing methods for controlling the peach fruit moth are not targeted enough, pesticide application is extensive, lack precise decision-making support, and cannot adapt to the complex climatic conditions in the Jiangsu and Zhejiang regions, resulting in poor control effects and excessive pesticide residues.

Method used

We constructed a multi-dimensional feature library and AI decision-making model specifically for peach orchards in Jiangsu and Zhejiang provinces. By combining visual recognition, sensor data, and phenological data, we achieved dynamic and precise matching of microemulsion pesticides, physical control, and auxin. Through weighted collaborative filtering and multi-objective optimization algorithms, we output the optimal control plan and established a closed-loop feedback mechanism optimization model.

Benefits of technology

It achieved a control efficacy of ≥90% for the peach fruit moth at all growth stages, reduced the amount of microemulsion pesticides used by 30-55%, lowered planting costs, adapted to complex climate changes, and improved control effectiveness and pesticide safety.

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Abstract

The invention discloses an AI-driven accurate prevention and control method for peach fruit moths in different growth periods, and relates to the technical field of crop disease and insect pest prevention and control. Aiming at the defects that peach fruit moth prevention and control pertinence is poor, pesticide application is extensive, and accurate decision and closed-loop optimization are lacked in peach orchards in Jiangsu and Zhejiang regions, a four-dimensional feature library containing crops, diseases and insect pests, environments and prevention and control means (each dimension is marked with a plant protection associated weight) is constructed; through a multi-mode AI model fused by vision, a sensor and phenological data, accurate judgment (the accuracy is greater than or equal to 95%) of the whole growth period of the peach fruit moths is realized; a weighted collaborative filtering and NSGA-III multi-objective optimization algorithm is adopted, an optimal combination of micro-emulsified pesticide, physical prevention and control and auxin is dynamically matched, and the goals that the prevention effect is larger than or equal to 85%, the pesticide decrement is larger than or equal to 30%, the cost is smaller than or equal to 15 yuan / mu, and the phytotoxicity rate is smaller than or equal to 0.5% are met; and a closed-loop system is formed through the field control effect feedback iteration model and the feature library. The compound fertilizer improves the yield and quality of peaches, reduces pesticide residues and cost, is suitable for the climate of Jiangsu and Zhejiang, and has a wide application prospect.
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