IoT and Edge Computing for Real-Time Monitoring and Predictive Analysis in Ostrich Hatcheries

Abstract

The use of Thing's Web (IWOT) and edge computing changes precision breeding, particularly in the management of Strain-Linkvation Centers. This study introduces an AI-operated IoT system that uses edge computing for real-time monitoring, data processing and predictive analysis in ostrich breeding. In contrast to traditional cloud-based systems that are exposed to limited bandwidth and high latency reliability issues, this system uses low latency devices to process sensor data directly on the source. This allows for accurate prediction of the success rate of slip parts by learning on devices using deep learning models. This is extremely important in environments where temperature, humidity and other factors directly affect life capacity. The system uses lighter IoT protocols such as MQTT and COAP, as well as spring-based learning to improve data security and reduce dependency on central cloud servers. Algorithms for machine learning, ambient conditions dynamically adapt based on historical patterns and real-time sensor data to optimize slip conditions. To ensure flexibility, the system uses container micro services using Cabernets. This allows for consistent delivery across a variety of IoT devices. Additionally, a blockchain-based smart contract system ensures data integrity, traceability and automated decision-making during incubator operations. Compared to the cloud model alone, it has been shown that the reaction time improvement is improved by 0%, and hatching chicks increases prediction accuracy by 25%. This research will facilitate the development of Agritech development for efficient automated incubator management through a combination of IoT, Edge AI, and blockchain. Future work will explore bioinformatics-based models for genetic optimization and expand the system to other precision livestock applications.