Application of Artificial Intelligence in Poultry Farming - Advancing Efficiency in Poultry Farming By Automating The Egg Counting Using Computer Vision System

Abstract

Abstract Counting eggs may seem like a simple task, but for poultry farms, it is a vital process that directly impacts productivity, inventory control, and overall output quality. However, the conventional manual counting methods are laborious, time-consuming, and prone to human errors. This research presents a ground-breaking computer imaging system designed to automate egg detection and counting, utilizing the remarkable potential of Computer Vision and Artificial Intelligence (AI) techniques. The primary objective is to develop a robust and reliable system capable of real-time identification and enumeration of eggs within poultry houses. Strategically positioned cameras capture images, providing a unique perspective into the poultry environment. State-of-the-art computer vision algorithms, including advanced object detection methods like Faster Regions with Convolutional Neural Networks (Faster R-CNN) or You Only Look Once (YOLO), accurately identify eggs within the images using cutting-edge deep learning models. By integrating AI techniques, the system enhances accuracy and reliability, while continuously learning from vast amounts of data. This transformative automation eliminates labour-intensive manual counting, offering a dependable, efficient, and cost-effective solution while reducing both time and labour requirements and minimizing human errors. Moreover, the automated system enables real-time data collection, facilitating data-driven decision-making in the poultry industry. Through the integration of cutting-edge computer vision algorithms and AI techniques, the system provides an accurate, efficient, and reliable solution to optimize production processes, enhance inventory control, and ensure high-quality outputs. This work contributes to the ongoing technological advancements in the poultry industry, ultimately improving productivity, and sustainability, and enabling data-driven decision-making.