AI-driven Smart Precision Farming Using Autonomous Drones and IoT Sensor Networks: A Case Study for Agriculture Automation

Abstract

Escalating global food demand, increasing climatic variability, and constraints on arable land and water resources necessitate the adoption of sustainable, data-driven agricultural systems. This study proposes a novel Smart Precision Farming 4.0 framework that synergistically integrates autonomous unmanned aerial vehicles (UAVs) with ground-based Internet of Things (IoT) sensor networks to enable high-resolution crop monitoring and intelligent decision support. The proposed architecture combines multispectral and thermal UAV imaging with distributed soil and environmental sensing, while artificial intelligence and machine learning (AI/ML) algorithms are employed for real-time analytics, crop-status classification, and predictive assessment of agronomic conditions. The methodology further incorporates mathematical models for soil-moisture estimation and crop-health indexing, alongside a comprehensive performance evaluation using quantitative metrics including detection accuracy, spatial coverage efficiency, and resource utilization effectiveness. Comparative assessment against conventional manual and semi-automated practices demonstrates substantial performance gains, including a 35–50% improvement in irrigation water-use efficiency, a 20–30% reduction in agrochemical inputs (fertilizers and pesticides), and approximately a 25% enhancement in early-stage detection of crop stress and disease onset. The findings validate that the proposed UAV–IoT–AI integrated approach significantly improves operational efficiency, sustainability, and responsiveness, thereby offering a scalable solution for next-generation precision agriculture.