Advancing Precision Livestock Farming: Integrating Hybrid AI, IoT, Cloud and Edge Computing for Enhanced Welfare and Efficiency

Abstract

Poultry farming is pivotal to global food security, yet maintaining optimal environmental and operational conditions remains a challenge. Suboptimal conditions, such as high temperature and humidity, promote bacterial growth and the production of toxic gases like ammonia (NH3), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and hydrogen sulfide (H2S), which increase poultry disease and mortality rates. This study introduces an innovative, modular, and scalable system integrating Artificial Intelligence (AI), Internet of Things (IoT), Edge Computing, and Cloud Computing for real-time monitoring, prediction, and automation in poultry barns. The system employs a hybrid AI framework combining Gradient Boosting techniques (XGBoost, LightGBM, CatBoost) and Long Short-Term Memory (LSTM) networks to analyze data from a heterogeneous wireless sensor network. It monitors critical parameters—temperature, humidity, and toxic gas concentrations—while predicting environmental conditions and detecting potential stress to optimize poultry welfare. Leveraging IoT for data collection, Edge Computing for low-latency processing, and cloud analytics for advanced insights, the system enhances decision-making, reduces feed wastage, lowers energy costs, and decreases mortality rates. A case study demonstrates significant improvements in prediction accuracy, operational efficiency, and animal welfare, underscoring the framework’s adaptability across diverse agricultural settings. This work establishes a robust precedent for hybrid AI-driven smart farming solutions, advancing precision livestock farming.