Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability

Artificial intelligence (AI) and machine learning (ML) are transforming agriculture by enabling data-driven decisions that elevate productivity and sustainability. This review synthesises 95 studies published between 2013 and 2023 that evaluate applications across crop monitoring, yield prediction, and resource optimisation. Reported model accuracies for neural networks, decision trees, and deep learning reached up to 93 percent; deep learning was most accurate but least interpretable. Reported benefits include a 25 percent increase in yield, a 28 percent reduction in costs, 40 percent efficiency gains, 22 percent water savings, 28 percent fertilizer savings, and 35 percent lower nitrogen runoff. Adoption barriers persist, including poor data quality, expensive infrastructure, limited digital literacy, and ethical concerns around data ownership and bias. Integrated, enterprise-scale platforms favor large farms, while mobile AI applications yield 15-30 percent gains for smallholders. Converging technologies blockchain, IoT, and robotics enable integration, and automation can lower labor and input requirements by 35 percent. The review points to the importance of inclusion policies, transparent systems, and global governance. Overall, AI/ML are drivers of socio-technical transition consistent with Sustainability Transitions Theory, necessitating multidisciplinary strategies for sustainable, climate-resilient food systems.