A method and system for plant leaf segmentation using synthetic data

A technology of plant leaves and synthetic data, applied in image data processing, image analysis, image enhancement, etc., can solve the problems of consuming a lot of manpower and financial resources, manual labeling errors, a large amount of training data, etc., to achieve good segmentation effect and reduce energy Consume and solve the effect of insufficient training data
CN110148146BActive Publication Date: 2021-03-02CHONGQING UNIV

Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Patents(China)
Current Assignee / Owner
CHONGQING UNIV
Publication Date
2021-03-02

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Abstract

The invention discloses a plant leaf segmentation method and system using synthetic data. The method includes: constructing a plurality of three-dimensional models of blades with different attitudes and colors based on the blade images; projecting the three-dimensional models of the blades onto a two-dimensional plane to generate two-dimensional image data of the blades; combining the two-dimensional graphic data of the blades with different background images Fusion is performed to obtain a training set; the deep learning model is trained through the training set to obtain a leaf segmentation model; an image to be segmented including leaves is input into the leaf segmentation model to obtain a segmented leaf image. Use the three-dimensional leaf model to generate segmentation data, reconstruct leaf models of different shapes and colors through a plant leaf image, and fuse them with different backgrounds to automatically generate a large number of training pictures and training labels to form a training set, reducing the The energy consumption of manually labeling images can fully automatically segment plant leaf images under natural background conditions, and the segmentation effect is good.
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Description

technical field

[0001] The invention relates to the field of image segmentation, in particular to a plant leaf segmentation method and system using synthetic data. Background technique

[0002] Studying plants, the identification, detection and segmentation of plant leaves is an important task for us in computer vision of plants. However, the background conditions of plant leaves under natural conditions are generally complex, and the types of plant leaves themselves have lines, colors, and textures. Therefore, it is a very difficult task to identify, detect, and segment plant leaves. Machines can effectively identify leaves, based on the ability to extract plant leaves from the natural background. Subsequent analysis of the plants can only proceed if the leaves are extracted from the background. For example, the segmented leaves can be used to determine whether the plant is growing healthily and whether it has suffered from diseases and insect pests. The segmentation alg...

Claims

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