Modified pressure imaging for egg crack detection and resulting egg quality

Current advances in detection of abnormal egg: a review

Internal and external defects of eggs should be detected to prevent cross-contamination of intact eggs by abnormal eggs during storage. Emerging detection technologies for abnormal eggs were introduced as an alternative to human inspection. The advanced technologies could rapidly detect abnormal eggs. Abnormal egg detection technologies using acoustic response, machine vision, and spectroscopy have been commercialized in the poultry industry. Non-destructive egg quality assessment methods meanwhile could preserve the value of eggs and improve detection efficiency. In order to improve detection efficiency, it is essential to select a proper algorithm for classifying the types of abnormal eggs. This review deals with the performance of the detection technologies for various types of abnormal eggs in recently published resources. In addition, the discriminant methods and detection algorithms of abnormal eggs reported in the published literature were investigated. Although the majority of the studies were conducted on a laboratory scale, the developed detection technologies for internal and external defects in eggs were technically feasible to obtain the excellent detection accuracy. To apply the developed detection technologies to the poultry industry, it is necessary to achieve the detection rates required from the industry.

Broiler stunned state detection based on an improved fast region-based convolutional neural network algorithm

An improved fast region-based convolutional neural network (RCNN) algorithm is proposed to improve the accuracy and efficiency of recognizing broilers in a stunned state. The algorithm recognizes 3 stunned state conditions: insufficiently stunned, moderately stunned, and excessively stunned. Image samples of stunned broilers were collected from a slaughter line using an image acquisition platform. According to the format of PASCAL VOC (pattern analysis, statistical modeling, and computational learning visual object classes) dataset, a dataset for each broiler stunned state condition was obtained using an annotation tool to mark the chicken head and wing area in the original image. A rotation and flip data augmentation method was used to enhance the effectiveness of the datasets. Based on the principle of a residual network, a multi-layer residual module (MRM) was constructed to facilitate more detailed feature extraction. A model was then developed (entitled here Faster-RCNN+MRMnet) and used to detect broiler stunned state conditions. When applied to a reinforcing dataset containing 27,828 images of chickens in a stunned state, the identification accuracy of the model was 98.06%. This was significantly higher than both the established back propagation neural network model (90.11%) and another Faster-RCNN model (96.86%). The proposed algorithm can complete the inspection of the stunned state of more than 40,000 broilers per hour. The approach can be used for online inspection applications to increase efficiency, reduce labor and cost, and yield significant benefits for poultry processing plants.

A real time quality control application for animal production by image processing

BACKGROUND

Standards of hygiene and health are of major importance in food production, and quality control has become obligatory in this field. Thanks to rapidly developing technologies, it is now possible for automatic and safe quality control of food production. For this purpose, image-processing-based quality control systems used in industrial applications are being employed to analyze the quality of food products. In this study, quality control of chicken (Gallus domesticus) eggs was achieved using a real time image-processing technique.

RESULTS

In order to execute the quality control processes, a conveying mechanism was used. Eggs passing on a conveyor belt were continuously photographed in real time by cameras located above the belt. The images obtained were processed by various methods and techniques. Using digital instrumentation, the volume of the eggs was measured, broken/cracked eggs were separated and dirty eggs were determined. In accordance with international standards for classifying the quality of eggs, the class of separated eggs was determined through a fuzzy implication model.

CONCLUSION

According to tests carried out on thousands of eggs, a quality control process with an accuracy of 98% was possible.

Salmonella contamination in shell eggs exposed to modified-pressure imaging for microcrack detection

Microcracks in egg shells are a food safety risk and are difficult for professional human graders to detect. Modified-pressure imaging technology with 99.6% accuracy has been developed to detect microcracks. This study was conducted to determine whether the microcrack detection system would increase penetration of Salmonella into egg contents or lead to cross-contamination within the system. Thirty dozen grade A large white retail eggs were used for each of 3 replicates. Cracked eggs were removed and 72 eggs/replicate were dip inoculated in buffered peptone water containing 10(5) cfu/mL of nalidixic acid-resistant Salmonella Typhimurium (ST), whereas 144 eggs were dipped in sterile buffered peptone water. All eggs were incubated overnight at 25°C before imaging. Forty-five eggs of each treatment were imaged in the following order: control, inoculated, control. Imaged and nonimaged eggs from each treatment were used for cultural analysis of a shell rinse, shell emulsion, and contents sample for each egg. The ST levels were monitored on brilliant green sulfa agar with 200 mg/L of nalidixic acid. Egg contents were also enriched to determine the prevalence of ST in low levels. Salmonella Typhimurium was not detected on or in any of the control eggs, including the eggs imaged after the inoculated eggs. The highest level of ST was detected in inoculated shell emulsions (4.79 log cfu/mL). No differences in ST levels were found for any sample location between imaged and nonimaged inoculated eggs. Therefore, the modified-pressure imaging system for microcrack detection did not result in microbial cross-contamination or increase the level of microbial penetration in inoculated eggs. The imaging system can be used to assess eggs for cracks without negative food safety implications.