A Computer Vision-Based Automatic System for Egg Grading and Defect Detection

Abstract

Defective eggs diminish the value of laying hen production, particularly in cage-free systems with a higher incidence of floor eggs. To enhance quality, machine vision and image processing have facilitated the development of automated grading and defect detection systems. Additionally, egg measurement systems utilize weight-sorting for optimal market value. However, few studies have integrated deep learning and machine vision techniques for combined egg classification and weighting. To address this gap, a two-stage model was developed based on real-time multitask detection (RTMDet) and random forest networks to predict egg category and weight. The model uses convolutional neural network (CNN) and regression techniques were used to perform joint egg classification and weighing. RTMDet was used to sort and extract egg features for classification, and a Random Forest algorithm was used to predict egg weight based on the extracted features (major axis and minor axis). The results of the study showed that the best achieved accuracy was 94.8% and best R2 was 96.0%. In addition, the model can be used to automatically exclude non-standard-size eggs and eggs with exterior issues (e.g., calcium deposit, stains, and cracks). This detector is among the first models that perform the joint function of egg-sorting and weighing eggs, and is capable of classifying them into five categories (intact, crack, bloody, floor, and non-standard) and measuring them up to jumbo size. By implementing the findings of this study, the poultry industry can reduce costs and increase productivity, ultimately leading to better-quality products for consumers.

Machine Vision-Based Surface Defect Detection Study for Ceramic 3D Printing

“…This paper presents the experimental results in terms of a confusion matrix to determine the robustness of the defect detection model in terms of recall, which is also as the true positive rate (TPR) for defective samples, and precision (PPV), which is also known as the positive predictive value (PPV) for samples that are predicted to be defective and reflects how many of the samples that are predicted to be defective are correct. Both recall and accuracy are important metrics for determining the robustness of a defect detection model and can prove that the defect detection model is correct [48][49][50][51]. The formulas for bubble recall and precision are shown below:…”

Illuminating Solutions for Reducing Mislaid Eggs of Cage-Free Layers

“…That is why they mandate that food producers have strategies to prevent mislaid eggs and ensure safe products. As a result, many food producers, researchers, and other agencies have adopted approaches like regular inspections [20], employee training [20], sanitation [21], and using technology like egg detection systems to detect defective eggs [22] and make their food products safer. Recent technologies, such as automated floor monitoring systems and computer vision, have also been developed to detect mislaid eggs [17,23,24] and floor egg-laying behavior (FELB) [5].…”