AI-Based Digital Disease Recognition Using Collaborative Conveyor Machine for Poultry Farming

This research presents a cutting-edge Al-driven digital disease identification system tailored to chicken husbandry. The system incorporates multiple sensors and enhanced object-detecting capabilities by leveraging collaborative conveyor machinery. The major goal is to improve disease detection precision in poultry habitats. The technology provides a complete method for recognizing potential health risks in chickens by painstakingly studying temperature changes and applying object detection algorithms using TensorFlow. The combination of artificial intelligence and sensor technology results in a potent tool for preventive disease control in the chicken business. This invention achieves some of the major sustainable development goals to kinetically monitor, protect and raise of chicks in day-to-day life, which are essentially named as Decent Work and Economic Growth, Industry Innovation and Infrastructure, Responsible Consumption and Production. The collaborative conveyor machine enables a coordinated and efficient procedure while collecting and analyzing data in real-time. This digital disease identification system marks a big step in optimizing chicken farming techniques due to the synergy of AI machine learning and sensor improvements. A collaborative conveyor machine with several sensors and item-detecting capabilities is used in this technique. The system is particularly designed to combat diseases such as fowl pox, avian paramyxovirus and coccidiosis. Using powerful AI algorithms, this system intends to increase illness detection accuracy and efficiency in chicken farming, resulting in better flock health management. The system promptly alerts farmers through GSM and IoT technologies, enhancing real-time monitoring and proactive management in poultry health.