Automated detection of defects and grading of chicken egg quality using the candling method, image processing, and machine learning

Abstract

In Sri Lanka, all efforts to maintain the safety, freshness, and market value of the eggs are done using manual grading. This is considering the listening method or the candling method of inspection, which tends to be very slow, subjective, and inaccurate, especially in bilk. This research posed the question of whether or not machine learning could be used to erase the manual grading of eggs and automate it based on the common defects adhering to SLSI guidelines. The goals were to create an automated grading system using candling, apply the machine learning process and determine the level of effectiveness of engineering created specifically for the field, and the defects in the eggs and grading. Ultrasounds, hyperspectral imaging, and deep learning have been used to inspect eggs. All of these methods are effective, but only for smaller and medium-sized poultry businesses. They are a bit impractical as they require massive sets of data and a large amount of computational power. Works like Yang et al (2023) have pointed out that SVMs stratified the data the best out of the models used, and they are much easier on the system and the data, so they keep the accuracy and effectiveness. From the commercial and university poultry farms, a set of 1,790 candling images was collected in which the eggs were filmed at 5 different angles and under steady light. Domain experts confirmed four defect types: cracks, irregular shapes, shell texture, and yolk outline visibility. Edge operator feature extraction (Canny, Sobel, and Laplacian), LBP and GLCM texture analysis, Gabor and wavelet shape descriptors, and Recursive Feature Elimination with Cross Validation (RFE-CV) feature subset optimizers. Trained individual hyperparameter-tuned binary SVM classifiers, and their outputs were a random forest for SLSI-based grades. The achieved defect detection accuracy and overall grading accuracy of the models were from 92.06 to 97.56 and 97.95, respectively. These models demonstrated strong performance and crossed 0.95 ROC-AUC scores. Statistical validation through stratified cross-validation, learning curves, and repeated trials confirmed overfitting minimisation and generalisation. The findings reaffirm the perceived notion that classical machine learning, combined with feature engineering, yields grade estimations that are accurate, efficient, and widely available without the need for complex deep learning. The system was transformed into a web application for farm workers to instantly grade uploaded candling images. With this, Sri Lanka’s poultry industry becomes scalable and loses net resource expenditure, a solution which minimises human error and ensures quality standards.