Scindapsus aureus leaf shape parameter estimation method based on MRE-Point Net and auto-encoder model

Abstract

The invention discloses a scindapsus aureus leaf shape parameter estimation method based on MRE-Point Net and an auto-encoder model, and the method comprises the steps: carrying out the photographingof scindapsus aureus from a single angle through a Kinect V2 camera, obtaining point cloud data, carrying out the preprocessing of the data through straight-through filtering, segmentation and point cloud simplification algorithms, building a scindapsus aureus leaf geometric model through a parameter equation, and calculating the blade length, the blade width and the blade area of the geometric model; and inputting the discrete point cloud data of the geometric model into a multi-resolution point cloud deep learning network to obtain a pre-training model, and taking the discrete point cloud data of the geometric model as input to obtain a pre-training model of an auto-encoder through encoding and decoding operation, performing secondary processing noise reduction is performed on input point cloud data through a pre-training model of an auto-encoder, and then parameter fine adjustment is performed on the pre-training model by using the measured scindapsus aureus leaf shape parameter label so that shape parameter estimation of the input scindapsus aureus leaf point cloud data can be completed.

Description

technical field [0001] The invention relates to the field of parameter equation modeling and deep learning, especially the estimation and analysis of plant phenotype parameters and the construction of pre-training models, in particular a method for estimating the shape parameters of pothos leaves based on MRE-PointNet and an autoencoder model. Background technique [0002] Plant phenotype refers to complex plant traits determined or influenced by genes and environment, including growth, development, tolerance, resistance, physiology, structure, yield, etc. Plant leaves are an important part of the external form of plants, and they are also the main organs of plants for their physiological functions. Leaf geometric parameters are not only important indicators of plant growth and development, yield formation and variety characteristics, but also important data support for reasonable cultivation management of crops and detection of pests and diseases. Therefore, accurate measur...

AI Technical Summary

Problems solved by technology

Input the discrete point cloud data of the geometric model into the multi-resolution point cloud deep learning network (MRE-PointNet) to obtain the pre-training model. For the problem of leaf occlusion noise, we use the discrete point cloud data of the geometric model as input through encoding-decoding operation Obtain the pre-training model of the autoencoder, and perform secondary processing and noise reduction on the input point cloud data through the pre-training model of the autoencoder. Perform parameter fine-tuning to complete the shape parameter estimation of the input point cloud data of pothos leaves

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