Segmentation method for trialeurodes vaporariorum image based on multi-feature fusion

Abstract

The invention discloses a segmentation method for a trialeurodes vaporariorum image based on multi-feature fusion. An information processing function of the human is simulated by the segmentation method for the trialeurodes vaporariorum image based on the multi-feature fusion. The segmentation method for the trialeurodes vaporariorum image based on the multi-feature fusion comprises the following steps of firstly, roughly finding out a possible pest region by the edge feature of the image; then, establishing a Gaussian mixture model of the difference of gray values of 8-connected neighborhoods in the region by utilizing the area, the perimeter, the minimum gray value, the maximum gray value, the average gray value, the gray value variance, the longest axis and the minimum enclosing rectangle of the pest region; modeling parameters of the Gaussian mixture model together with other features; repeatedly rejecting samples which does not fit model distribution; and extracting a real pest target.

Description

technical field [0001] The invention relates to the field of automatic recognition of images of diseases and insect pests, in particular to a whitefly image segmentation method based on multi-feature fusion. Background technique [0002] With the application and development of machine vision and image processing technology, and the improvement of computer software and hardware, it is possible to use digital image processing technology to process, segment and identify images of crop diseases and insect pests, and to realize automatic identification of diseases and insect pests. Therefore, machine vision technology as a An important automatic identification method of pests has attracted people's attention and has been widely used in the field of pest control. [0003] The population density and damage degree of pests on crops are an important basis for decision-making of pest control, and also the key information for precise spraying. Compared with manual methods, the use of ...

AI Technical Summary

Problems solved by technology

At present, one of the difficulties in pest detection and technology is: in the open field environment, the environment is complex, the background color changes, and the gray scale ranges of the background, leaves and pests often overlap, which makes automatic segmentation of pests difficult

At present, more studies are made on the threshold method and the combination of threshold and clustering. In the open field environment, it will inevitably lead to mis-segmentation. Therefore, it is imminent to find a method that can accurately segment pests in the open field environment.

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