An Improved Poultry Farm Health Prediction System based on Artificial Intelligence Assisted Hybrid Deep Learning Model

Poultry production is a crucial factor in worldwide economic development, significantly enhancing human ecological resilience. It provides economical and easily obtainable protein supplies, including meat, eggs, and various by-products. Poultry farming not only provides nutritional advantages but also increases household income, strengthens food security, and contributes to poverty alleviation, so playing a crucial role in global economic progress. Poultry meat and eggs are in high demand due to the growing global population. Poultry disease management concurrently presents a critical problem, posing substantial risks to food security and economic stability. Utilizing advanced technology presents opportunities to develop techniques that enhance farm profitability while reducing environmental effects and promoting the welfare of animals and humans. This paper presents an Artificial Intelligence (AI) assisted method termed the Coccidiosis Disease Prediction Strategy (CDPS) for effectively assessing poultry farm health, which is cross-validated against the traditional learning model known as Convolutional Neural Network (CNN) to evaluate the efficacy of the proposed approach. The experimental research demonstrates that novel technology solutions, particularly image processing and advanced deep learning, significantly surpass traditional manual inspection techniques in the early identification and warning of diseases in the chicken industry. These developments highlight their potential to transform poultry health administration and preventing illness.