Deep Learning Based Egg Size Identification for Poultry Farming

Egg size classification holds significant importance in poultry farming as it directly impacts production, quality evaluation, and consumer preferences. This paper introduces a novel automated approach that utilizes the capabilities of convolutional neural networks (CNNs), specifically employing a pre-trained MobileNetV2 model, to precisely sort eggs according to their sizes. The method benefits from a well-curated dataset of egg images, each meticulously labeled with their respective sizes. The CNN architecture adeptly extracts intricate features from these images and learns patterns, leading to a substantial enhancement in the accuracy of egg size classification. Accurately measuring the size of eggs is of utmost significance in the poultry sector, impacting various aspects such as production efficiency, product quality, and customer satisfaction. Traditional manual methods for classifying egg size are not only time-consuming but also prone to human errors, necessitating the development of automated solutions. The proposed system harnesses the capabilities of deep convolutional neural networks (CNNs) to automatically determine the size of chicken eggs. To facilitate this, an assembled a comprehensive dataset of egg images, meticulously annotated with size labels, and subjected them to preprocessing. These images serve as the training data for the CNN model, enabling it to learn the intricate features and patterns associated with different egg sizes. The performance of the trained model in detecting egg size is notably impressive, achieving an accuracy rate exceeding 85% on the test dataset. It reliably categorizes eggs into predefined size classes such as small, medium, and large, offering real-time assessments with minimal errors. The implications of this research extend far and wide, providing the poultry industry with an efficient and accurate tool for classifying egg size. The automated egg size detection using deep learning has the potential to streamline production processes, reduce labor costs, and enhance the quality of products delivered to consumers.