Plant field pest fine-grained recognition method, system and device based on deep learning and storage medium

CN120913167BActive Publication Date: 2026-04-21ZHEJIANG UNIV
View PDF 1 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ZHEJIANG UNIV
Filing Date
2025-07-17
Publication Date
2026-04-21

AI Technical Summary

Technical Problem

Existing pest identification methods are difficult to classify pests with similar morphologies accurately in the field, resulting in low identification precision and being time-consuming and labor-intensive, which cannot meet the needs of agricultural production.

Method used

A deep learning-based approach is adopted, using the MaxViT model combined with a deformable self-attention mechanism and a contrastive learning loss function to train a plant field pest identification model. End-to-end fine-grained classification is performed through convolutional modules, MaxViT-DAT attention modules, global average pooling modules, and fully connected layer modules.

Benefits of technology

It enables fine-grained classification of various morphologically similar pests with an identification accuracy of over 90%, improving the efficiency and precision of pest identification and making it suitable for agricultural pest surveys and insect ecological surveys.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN120913167B_ABST
    Figure CN120913167B_ABST
Patent Text Reader

Abstract

The present application relates to the technical field of intelligent identification system of crop pests, in particular to a plant field pest fine-grained identification method, system, device and storage medium based on deep learning. The identification method provided by the present application is specialized in high-precision identification of real field scenes, and can provide technical support for important work such as future development of field inspection robot, automatic identification and monitoring system of field pests and the like. In addition to pest monitoring, the field biological safety test of genetically modified plants is gradually carried out at present, and by using the identification method provided by the present application, the dynamic change of farmland insect community can be quickly and accurately identified and predicted, so that the efficiency and accuracy of ecological investigation are greatly improved.
Need to check novelty before this filing date? Find Prior Art

Citation Information

Patent Citations

  • Unsupervised echocardiogram section identification method

    CN115578589A