| The key technologies of the online intelligent detecting system for egg quality were studied to improve the ability of egg postharvest handling and synchronization detection for multi-index. The research achievement can be used to break the monopolization of foreign products, and make contributions to develop automation system for egg primary processing. In this study, the bottleneck and the key technical problems, which arised with the application of acoustic technique, computer vision and dynamic weighing in egg quality intelligent detection system, was studied to realize the detection of external quality (eggshell strength, cracks, size, weight and cleanliness) and internal quality (freshness) of chicken eggs. Due to the complexity of mechanical device and instability of multi-detection in the existing system, surface wave analysis was studied. In order to achieve the crack detection with one impact, the distribution, spread and attenuation of the wave on eggshell surface were analysed. The main achievements are summarized as follows:1. Development of on-line measurement module for eggshell quality. An on-line system based on acoustic resonance with digital signal processing (DSP, TMS320C2812) as the core processors was developed for the measurement of eggshell quality. Scroll forward of egg, automatic impact and the acquisition, processing and analysis of the response signal can be achieved based on the on-line module. The effects of excitation point, speed of the conveying system, impact intensity and egg mass on the frequencies response signals were investigated to acquire stable and higher signal-to-noise ratio signals. Fast Fourier transformation was employed to transform the signal from time domain to frequency domain. Spectrum analysis for the sound signal was made for finding out remarkable frequency factors. Qualitative and quantitative analysis were conducted to built the detection modle for eggshell quality. The identification rates of intact eggs and cracked eggs were97.27%and94.17%, respectively. The correlations coefficients (R) between the strength measured by references method (Quasi-static measurement) and acoustic response signal was0.776, and mean square error of prediction was3.01N.2. Improvement and optimization for the eggshell quality automatic detection system. According to the previous research, signal acquisition and processing, automatic impact and upper computer submodule were improved to meet the requirement of automatic and industrialized production. TMS320C5509A was used as the core processor in signal acquisition and processing submodule. In this way, the process of signal acquisition and signal modulating can be simplified. Meanwhile, their resistance to interference and processing speed were improved in the module. By improving the control method of automatic impact, the detection stations can be arranged closely. Therefor, the space of eggshell crack detection module can be shortened. By analysing the features of sound signal from intact and cracked eggs, zero-pass points of the time siganl was computed as the characteristic parameter to discriminat intact and cracked eggs. The identification rates of intact eggs and cracked eggs were97.58%and95.76%, respectively.3. Development of on-line measurement system for egg’s external physical characteristic. The software was developed based on Visual Studio2008+QT on computer. The image of eggs in motion were acquired by CCD camera, and processed in realtime. Long axis, minor axis and cleanliness of eggshell were the three output parameters of the system. Eggs were segmented using global threshold in G-B image. In the reasesrch, after the comparing of different size detection methods, enclosing rectangle mothed was determined to compute the long and minor axis. Meanwhile, the egg images in different color spaces were studied to detecion cleanliness of eggshell. The correlations coefficients (R) between the long and minor axis measured by references method and image process were0.9477and0.9185, and average absolute error of prediction were0.5393mm and0.3658mm. The detection rate of eggshell with blot was94.74%4. Development of on-line measurement system for egg’s weight. Roller with Y type carries the egg forward. When the egg passes by the weighing mechanism, a software program was written based on Labview that allows a fast acquisition and processing of the dynamic signal. A fifth order Butterworth lowpass filter with the cut-off frequency of20Hz was employed to process the time signal. Then, a section of the filted signal (between160ms to200ms)was cut out to compute the egg’s weight. Based on the linear model which built for egg’s weight prediction, the error between predicted and real ranges from-0.75g to0.66g.5. The study of egg freshness based on physical method. Researches have showed that freshness of eggs is significantly correlated with the density. Therefore, three parameters of long axis, minor axis and weight, which are computed by machine vision technology and weigh-in-motion technology, were used to build multivariate linear model to compute Haugh Unit. The result showed that, the correlations coefficients (R) between the predicted and reference values was0.8653, and average absolute error was2.9874.6. Eggshell crack detection based on the surface wave analysis. A system based on surface wave analysis, as the basic experiment, was developed to detect eggshell crack. It was achieved by analysis the distribution, spread and attenuation of the wave on eggshell surface excited by a light mechanical. A software program was written in Labview that allows a fast acquisition and processing of the response signal. Seven features variables were exacted from response frequency signals and the scores vectors were extracted byPrincipal component analysis (PCA). In the reasearch, the eggshell was divided into eight equal parts to explore the influence caused by the different locations of crack. The result showed that, surface wave analysis was a feasible approach to detect the crack on the eggshell, and can reduce the detecion time.In this work, some key technologies for the online intelligent detecting system of egg quality was studied, and some different detection modules were designed to detect different indexs of egg quality. The research achievement can further improve the reliability and detection precision of egg quality detection system. It was of actual practical significance in improving automatic detection system and grading equipment. It also can set the stage and guides to online detection system for other agricultural products. |
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