基于机器视觉的大田害虫检测系统

设计了大田害虫实时检测系统，诱集传输机构可自动诱集并调整害虫姿态，使其平稳经过摄像视区；光照及采集系统可提供均匀的无影光照，并实时采集害虫的序列图像信息；运用修正的自适应邻域平均法增强图像，用自动阈值法将害虫从背景中分割出来，以提取出的周长、不变矩等为特征，运用神经网络分类器对常见的9种害虫进行分类。试验验证了该系统的可行性。

Design of Detection System for Agriculture Field Pests Based on Machine Vision

The design of a real-time detection system for agriculture field pests was developed. The collecting-conveying device could collect and adjust the posture of pests automatically, and send them into the area of CCD camera with proper speed. The chamber and collection system could produce even and invariable light, and collect series of images information at real time. The self- adaptive enhancement techniques were used to improve the images of agriculture field pests sample, the pests image can be separated from the background by using the auto-threshold method. The characters of a pest, such as perimeter, invariant quadrature, and so on, were taken as its characteristics to identify nine types of pests by using a classifier based on BP NN. The experiment shows that the system is practical and feasible.