Pest identification method based on established multi-characteristic database

Abstract

The invention discloses a pest identification method based on an established multi-characteristic database. According to the method, firstly, blocked background reconstruction for field images or video images is carried out, for better adapting to background change, the image background is adaptively updated, a background difference method is utilized to detect moving crop pests to acquire crop pest images, a mathematical morphologic method is utilized to carry out void processing on the pest images; and secondly, after pest shape characteristic parameters are extracted, the multi-characteristic database is established and trained, through combining the crop pest multi-characteristic database, farmland crop pest identification is accomplished. The method is advantaged in that as proved, farmland crop pests can be excellently detected, and the method can be effectively applied to crop pest detection and prevention.

Description

technical field [0001] The invention belongs to the technical field of video monitoring and image processing, and relates to a method for detecting and identifying farmland crop pests based on background difference and combining multi-feature databases. Background technique [0002] my country is a large agricultural country, and agricultural pests also occur from time to time, so the monitoring of crop pests in farmland and the statistical forecasting of pest disasters are very important. If the monitoring and forecasting is accurate and timely, pests can be eliminated early and the amount of pesticides can be reduced. At present, black light trapping and manual identification are widely used to count the species and density of pests. This method is labor-intensive, low in efficiency, and subjective factors are relatively large, which affects the accuracy and timeliness of forecasting. Therefore, the real-time and accurate detection of farmland crop pests is an inevitable ...

AI Technical Summary

Problems solved by technology

The advantage of the optical flow method is that it can detect moving objects under the premise of camera movement, and does not need to know any information about the scene, but the optical flow method has high computational complexity and poor anti-noise performance. Achieve real-time processing of video streams; the inter-frame difference method is very suitable for dynamically changing environments, but the inter-frame difference method is not sensitive to light changes, and generally cannot completely extract complete motion feature pixels, and it is easy to produce voids inside moving entities It is not conducive to further object analysis and recognition; the background subtraction method is the most commonly used method in motion detection, and it can completely segment moving objects when the camera is still and the background is relatively fixed.

However, in practical applications, due to factors such as scene lighting changes and camera shake, the background needs to be updated and maintained in real time. Therefore, the difficulty of the background difference method lies in the update and reconstruction of the background.

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