Machine learning has emerged as an effective tool for classifying fertile and infertile eggs before the incubation process, a development with significant implications for poultry farmers worldwide. This innovative approach, using a line scan hyperspectral imaging system, holds the promise of increasing hatchability rates and curbing the wastage of non-fertile eggs.

Unmasking Fertility: A Technological Leap

A study involving 227 eggs from Leghorn laying breeder hens was conducted, with conditions such as hen age, feeding, and management all controlled to ensure that the only variable was the presence of an embryo. The Vis-NIR HSI system captured spectral images in the 400-1000 nm range, but this was later narrowed down to 500-950 nm due to light absorption and signal-to-noise ratio considerations.

Machine Learning: The Future of Fertility Classification

Image processing and feature extraction were handled using MATLAB, with various preprocessing techniques and machine learning tools compared. These included Soft Independent Modelling of Class Analogy (SIMCA), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Artificial Neural Network (ANN) methods. The goal was to classify eggs based on fertility, a task that has traditionally been challenging due to the initial stages of development being difficult to detect via the customary candling method.

Implications for the Poultry Industry

Detecting infertile eggs before incubation carries immense importance for the hatchery industry. Annually, billions of non-fertile eggs are incubated, leading to economic loss, energy inefficiency, and potential contamination. The utilization of hyperspectral transmittance imaging suggests a potential solution to this problem, offering a cost-effective way to discern between fertile and infertile eggs before incubation. This study not only highlights the application of spectral analysis in poultry farming but also underscores the growing role of technology in enhancing agricultural efficiency.