A Review on Precision Agriculture: Leveraging Variable Rate Application and Machine Learning for Sustainable and Profitable Farming

Abstract

This review paper observes the transformative part of precision agriculture (PA) in recent farming, with a focus on Variable Rate Application (VRA) a essential component of PA that allows site-specific input spreading for seeds, water, fertilizers, and also the pesticides. It explores the addition of cutting-edge technologies, together with the Global Positioning System (GPS), remote sensing, Internet of Things (IoT) sensor networks, in addition artificial intelligence (AI)-driven machine learning (ML) models, which together empower VRA and transform farm management. These technologies ease real-time monitoring of soil features, crop health, also environmental conditions, enhancing resource usage and enhancing crop yield besides quality. Drone-based remote sensing and high-resolution satellite imagery allow for thorough field condition assessments, early detection of pest outbreaks, nutrient deficiencies, and water stress. While machine learning algorithms combine various data streams to predict input requirements and automate responses, transforming practices from reactive to proactive, GPS-guided machinery guarantees precise spatial accuracy. World case studies, such as drone-guided irrigation in Australian wheat fields, sensor-based pesticide application in Indian rice paddies, and AI-enabled fertilization in the US Midwest, show the useful advantages of these integrated systems. By increasing yields by 10–15% and lowering input costs by up to 30%, VRA adoption has been demonstrated to increase profitability and strengthen farming operations' financial stability. Additionally, by minimizing chemical overuse, limiting nutrient runoff, and conserving water resources, VRA supports environmental sustainability. Despite these obvious benefits, there are still obstacles to adoption, such as inadequate infrastructure, limited funding, and low levels of technological literacy. This review discusses policy strategies— financial incentives, farmer education, in addition the development of user-friendly VRA systems tailored to smallholder contexts—to address these challenges. Along with outlining potential future developments, it highlights the vital role that VRA systems play in creating robust and sustainable food production systems. These developments include autonomous machinery, blockchain-enabled supply chains, and next-generation predictive analytics.