A method and system for plant leaf segmentation using synthetic data

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.

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...

AI Technical Summary

Problems solved by technology

However, based on the method of deep learning, there must be training data. At present, the public data sets in the world are manually labeled. Manually labeled data means that it consumes a lot of human and financial resources, and manual labeling is also easy. labeling error

Therefore, the lack of data has seriously hindered the progress of deep learning methods in image segmentation algorithms.

And so far, there is no segmentation dataset specifically for plant leaves

At the same time, the lack of data also hinders the development of deep learning

[0005] It can be seen from the above that using the traditional leaf segmentation method requires manual input of some parameters, and automatic segmentation cannot be completed, or the segmentation effect is poor.

The segmentation method using deep learning can achieve fully automatic segmentation of leaves, but requires a large amount of training data

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