A systematic literature review on artificial intelligence in transforming precision agriculture for sustainable farming: Current status and future directions

Abstract

Agriculture encounters significant challenges, with the demand to increase food production by 50% by 2050 to sustain a growing global population while tackling the impacts of climate change and resource scarcity. Artificial intelligence (AI) has transformative potential for precision agriculture, optimizing crop management, resource allocation and sustainable farming practices. A systematic literature review (SLR) was conducted using the Scopus database, initially identifying 8145 articles. Based on eligibility criteria, 76 were selected for in-depth analysis. This paper focuses on AI applications in key areas of agriculture, including crop monitoring, irrigation management, weed and pest control, yield prediction, and smart spraying technologies. AI-driven techniques, such as machine learning, computer vision, robotics and the Internet of Things (IoT), enhance agricultural productivity and sustainability through data-driven decision-making and real-time monitoring. AI-based irrigation systems optimize water use efficiency by integrating sensor inputs with weather data, while robotic technologies enhance targeted weed and pest management. Resource efficiency is further enhanced by smart sprayers and yield estimation techniques. Despite these advancements, research gaps remain, particularly in integrating AI with emerging fields such as nutrient management and expanding the use of sensor systems. This paper highlights advancements in AI for precision agriculture, including crop monitoring, irrigation management and yield prediction, while identifying gaps in areas like nutrient management and sensor integration. Addressing these gaps is essential for developing more sustainable and resilient agricultural systems.