Is AI for illiterate farmers? A systematic literature review of AI and machine learning applications and challenges for precision agriculture

This study systematically reviews the applications, benefits, and challenges of artificial intelligence (AI) and machine learning (ML) in poultry farming. The research sought to answer the following key questions: How have AI/ML technologies been applied in poultry farming? What benefits have they provided? What challenges hinder their adoption among illiterate farmers? The review followed a structured methodology that adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A structured search on Science Direct and Google Scholar identified 1168 articles, from which 39 peer-reviewed studies (2010–2024) were selected. The selection process ensured methodological rigor through well-defined inclusion and exclusion criteria. Findings show significant AI/ML adoption in poultry farming with deep learning models (YOLO, CNNs) achieving over 90% accuracy in disease detection and health monitoring. Traditional ML methods (Random Forest, SVM) were discovered to improve feeding efficiency and genomic predictions, while AI-driven supply chains optimized inventory and reduced costs. However, challenges such as poor data quality, computational demands, scalability issues, and lack of explainable AI (XAI) and assistive AI (AAI) were identified as barriers to adoption especially among illiterate farmers. The research found that lightweight, adaptable AI models are essential for resource-constrained environments. In addition, longitudinal studies are needed to assess AI/ML’s long-term impact in poultry farming in different domains. Future research should also explore real-time pathogen genome sequencing and blockchain integration to enhance biosecurity and transparency in the poultry industry.