Article’s

Precision agriculture has developed as an important technique for solving global food security concerns while also improving environmental sustainability, resource conservation, and economic prosperity. Precision farming is a scalable and sustainable method to improving agricultural resilience and productivity through data-driven farm management, as agricultural systems confront increasing pressures from population increase, climate change, and depleting natural resources. The incorporation of Artificial Intelligence (AI) into precision agriculture has hastened the shift to intelligent and sustainable farming systems. AI-powered solutions provide real-time monitoring, predictive analytics, and automated decision-making, which improves agricultural efficiency and reduces waste. Among these innovations, smart irrigation systems utilize soil moisture sensors, weather forecasting models, and IoT-enabled monitoring devices to optimize water application, resulting in improved water-use efficiency and enhanced crop productivity. Similarly, unmanned aerial vehicles (UAVs), commonly known as agricultural drones, have become indispensable tools for crop surveillance, disease detection, nutrient assessment, weed identification, and precision pesticide application, enabling farmers to monitor field conditions with high spatial and temporal resolution. This study investigates the role of Artificial Intelligence (AI) in promoting sustainable agricultural practices through precision farming technologies. Specifically, it examines the adoption of AI-enabled smart irrigation systems and various categories of agricultural drones used for field monitoring, crop health assessment, and resource management. The empirical research is based on 116 primary survey responses from farmers from various sections of Telangana State, providing information about their awareness, adoption, and perceptions of AI-driven precision agriculture. To validate and contextualize these findings, the study compares primary survey responses to secondary market data from 2023 acquired from Market.us. Exploratory Data Analysis (EDA) is used to compare AI-driven precision agriculture against conventional farming approaches. EDA provides a strong analytical framework for detecting hidden patterns, trends, anomalies, and correlations in both primary and secondary datasets. By combining farmer-level data with industry market figures, the study provides a thorough knowledge of AI adoption, field variability, and resource use. The findings promote evidence-based decision-making, increase operational efficiency, and highlight AI technology’ promise for promoting sustainable, resilient, and data-driven agricultural production systems. Keywords: Precision Farming, Artificial Intelligence, Exploratory Data Analysis, Analytics