AI-Driven Precision Agriculture: Analysing Its Impact on Crop Yield, Sustainability, and Farm Efficiency

Farming is undergoing a major shift with the rise of AI-driven precision agriculture. By combining technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), modern agriculture is becoming more efficient, sustainable, and responsive to real-world challenges. These tools help farmers make smarter decisions—whether it is adjusting irrigation based on real-time data or using drones to monitor crop health. In this study, we explore how AI is changing the way farming works. Our focus is on understanding how these technologies improve yields, conserve resources, and support sustainable agricultural practices through predictive analytics, automation, and data-driven insights.This study adopted a mixed-method research approach that utilized both primary and secondary data sources. Primary data included case studies of AI-integrated farms, while secondary data were gathered from scholarly articles, industry reports, and government publications. Machine learning models, IoT-based sensors, and drone technologies were assessed for their effectiveness in yield prediction, soil health monitoring, and automated farming solutions. Statistical and comparative analyses were employed to evaluate the impact of AI on productivity, cost efficiency, and environmental conservation.The findings indicate that AI-driven precision agriculture significantly enhances crop yield by 15-30%, reduces water consumption by up to 40%, and minimizes pesticide usage by 20-50%. Furthermore, AI-powered automation improves farm efficiency and reduces labour and operational expenses. However, challenges, such as high initial investment, data security concerns, and the need for technical expertise, hinder widespread adoption. Addressing these barriers through policy support and farmer education can accelerate AI adoption in sustainable farming.