Comparative Analysis of Long Short-Term Memory and Gated Recurrent Unit Models for Chicken Egg Fertility Classification Using Deep Learning

This study explores the application of advanced Recurrent Neural Network (RNN) architectures—specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—for classifying chicken egg fertility based on embryonic development detected in egg images. Traditional methods, such as candling, are labor-intensive and often inaccurate, making them unsuitable for large-scale poultry operations. By leveraging the capabilities of LSTM and GRU models, this research aims to automate and enhance the accuracy of egg fertility classification, thereby contributing to agricultural automation. A dataset comprising 240 high-resolution egg images was employed, resized to 256 × 256 pixels for optimal processing efficiency. LSTM and GRU models were trained to discern fertile from infertile eggs by analyzing the sequential data represented by the pixel rows in these images. The LSTM model demonstrated superior performance, achieving a validation accuracy of 89.58%, significantly surpassing the GRU model (66.67%). Compared to classical methods such as Decision Tree (85%), Logistic Regression (88.3%), the LSTM model demonstrated superior performance, achieving a validation accuracy of 89.58%, significantly surpassing the GRU model (66.67%). Compared to Decision Tree (85%), Logistic Regression (88.3%), SVM (84.57%), K-means (82.9%), and R-CNN (70%), the LSTM model achieved the highest classification accuracy. Unlike classical machine learning approaches that rely on handcrafted features and predefined decision rules, LSTM effectively learns complex sequential dependencies within images, improving fertility classification accuracy in real-world poultry farming applications. In contrast, GRU models, while more computationally efficient, may struggle with generalization under constrained data conditions. This study underscores the potential of advanced RNNs in enhancing the efficiency and accuracy of automated farming systems, paving the way for future research to further optimize these models for real-world agricultural applications.