关键词：种蛋筛选;成活性检测;机器视觉;图像处理;遗传算法;

微粒群算法;神经网络

Study on Automatic Identifying Quality and Fertility of Hatching Egg

Based on Machine Vision System

Abstract

Identifying quality and fertility of hatching eggs are an important and hard work in the farms. Manual inspection suffers from visual stress and tiredness and is low accuracy and time-consuming. An automatic and practical detection system based on machine vision system and ANN is developed instead of manual inspection of hatching egg for improving detecting accuracy and effciency.

1. The machine vision hardware system is built for identifying exterior quality and fertility of hatching egg .The light source and background color are found out through a lot of experiments. Camera calibration is done for correcting image distortion, and its accuracy is able to match the demand of identifying exterior quality of hatching egg.

2. Based on machine vision technique, criterion is proposed for comprehensive evaluating egg’s exterior quality by weight, shape, eggshell defect feature and eggshell color, and method of egg quality classification is developed.

(1) The projection area of egg image is extracted by 0-order moment and used to classify egg weight instead of metage. The classification accuracy is 97.73% for bigger eggs, 97.04% for normal eggs, and 96.51% for smaller eggs.

(2) Threshold recognition and 8-connected boundary tracking method are combined to extract the defect feature on eggshell, and its classification accuracy is 91.25% for cracked eggs, 94.18% for dirt stained, blood spotted eggs and 96.36% for normal eggs.

(3) Egg shape index and radius differences are extracted as shape feature parameters, a two-step shape measurement method is proposed based on machine vision, moment technique and neural network. An improved immune GA algorithm is put forward, which is used to optimize topology structure of LMBP neural network for detecting quality of hatching egg automatically.

Key Words:  Fertility identification;  Image processing;

1  引言 1

1.1  机器视觉概述 1

1.2  研究背景和意义 1

1.3  国内外研究现状 2

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1.5.1  种蛋筛选和孵化成活性检测硬件系统建立 错误!未定义书签。

1.5.2  基于机器视觉的种蛋筛选 错误!未定义书签。

1.5.3  基于机器视觉的种蛋孵化成活性检测 错误!未定义书签。

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