An invasive alien plant detection method based on improved YOLOv9 algorithm
By introducing the DynamicConv module and TripletAttention mechanism into the YOLOv9 algorithm, combined with the MPDIoU loss function and data augmentation techniques, the detection model for invasive alien plants was optimized, solving the problems of insufficient detection accuracy and efficiency in existing technologies, and achieving efficient and accurate plant identification.
CN119399688BActive Publication Date: 2026-02-13AGRICULTURAL GENOMICS INSTITUTE AT SHENZHEN CHINESE ACADEMY OF AGRICULTURAL SCIENCES (SHENZHEN BRANCH GUANGDONG LABORATORY FOR LINGNAN MODERN AGRICULTURE) +1
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Patent Information
- Application Number
- CN202411444282.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-16
- Publication Date
- 2026-02-13
- Estimated Expiration
- 2044-10-16
AI Technical Summary
Technical Problem
Existing YOLO algorithms lack sufficient accuracy and efficiency when dealing with high-density plant populations or similar species, making it difficult to achieve efficient and accurate detection of invasive alien plants.
Method used
Based on the YOLOv9 algorithm, the DynamicConv module is introduced to replace the downsampling module, the TripletAttention attention mechanism is embedded, and the MPDIoU loss function is used. Combined with multi-stage learning rate adjustment and data augmentation techniques, the network model is optimized.
Benefits of technology
It significantly improves the accuracy and robustness of detecting invasive alien plants, adapts to complex backgrounds and different light environments, and achieves efficient and accurate detection results.
✦ Generated by Eureka AI based on patent content.
Abstract
The application discloses an alien invasive plant detection method based on an improved YOLOv9 algorithm, and the steps of the method are as follows: 1) acquiring images of alien invasive plants; 2) labeling the acquired alien invasive plant images, constructing an alien invasive plant dataset, and dividing the dataset into a training set, a verification set and a test set according to a certain proportion; 3) establishing an improved YOLOv9 network model; 4) training the improved network model; 5) detecting the images of the alien invasive plants by using the trained model, obtaining a detection result, and evaluating the test effect. The alien invasive plant detection method based on the improved YOLOv9 algorithm has the technical advantages and practical application value of high efficiency and accuracy in monitoring the alien invasive plants by improving and optimizing the YOLOv9 algorithm, and is especially suitable for large-scale monitoring and rapid response to the environment, and has important significance for ecological protection and agricultural safety management.
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