Introduction : The agriculture sector is a crucial driver of global economic growth, especially in the face of the increasing demand for food production to sustain a growing population. Traditional farming methods fall short of meeting these demands sustainably. Context: In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as pivotal tools for agricultural management, promising higher productivity and profitability. Objective: This systematic review provides a comprehensive overview of AI and ML applications, their utility, and algorithms in agriculture. Content: AI and ML technologies empower autonomous learning and enhance decision-making processes. They facilitate smart monitoring of crops and soil, transforming agriculture by optimizing resource utilization both on and off the field. These technologies play a vital role in crop development and production, protecting crops from various biotic and abiotic threats. This review delves into a diverse range of AI applications in agriculture, including the use of sensors, robotics, and drones to improve agricultural operations. These innovations hold the potential to efficiently manage water, reduce the need for herbicides, pesticides, and fertilizers, and minimize manual labor, ultimately boosting productivity and enhancing crop quality. Significance: In a world where food security and resource efficiency are paramount, this review underscores the necessity of harnessing AI and ML for the advancement of precision agriculture and resource management.

Keyword: Precision Agriculture, Artificial intelligence, Deep learning, Drone, Machine learning, Sustainable development, AI and AEAS