The advancement of smart agriculture technologies has opened new frontiers in livestock management, particularly in poultry farming. This study presents a practical case on the deployment of smart sensors and artificial intelligence (AI) models to monitor and optimize the health, growth, and productivity of broiler and noiler chickens from day-old chicks to harvest. Traditional poultry farming methods often lack real-time data and predictive analytics, resulting in inefficiencies in feed management, disease detection, and environmental control. In this study, a smart poultry monitoring system was conceptualized and tested using environmental sensors (temperature, humidity, ammonia levels), weight tracking devices, and camera-based behavioral monitoring. The collected data were processed and analyzed using AI algorithms including decision trees and artificial neural networks to detect anomalies, forecast weight gain, and recommend timely interventions. Results showed that the smart system significantly improved feed conversion ratios and reduced mortality rates by enabling early detection of health issues. The model also provided actionable insights to optimize the production cycle, enhance biosecurity, and improve animal welfare. This approach supports data-driven decision-making in poultry farming and aligns with the principles of precision agriculture for sustainable food production. The findings demonstrate the potential for broader application in resource-limited settings, offering a replicable model for smart poultry farming across sub-Saharan Africa and beyond.
Banjoko et al. (2025) studied this question.