Dual-Purpose Aerial Imaging using Drone for Precision Agriculture and Rapid Disaster Response

Agriculture, a cornerstone of global sustenance and economy, is increasingly threatened by climate variability, nutrient degradation, pest outbreaks, and unforeseen natural disasters. To mitigate these challenges and enhance both crop productivity and disaster resilience, this paper presents an integrated drone-based system titled “Precision Agriculture and Rapid Disaster Response Using Drone Technology.” The primary objective is to support farmers and society with real-time, actionable data for improving crop yield, detecting plant diseases, and enabling swift disaster management. The proposed system employs Unmanned Aerial Vehicles (UAVs) equipped with high- resolution cameras and environmental sensors to perform dual roles: continuous agricultural monitoring and dynamic disaster response. For agricultural analysis, we utilize advanced deep learning models, particularly the YOLO (You Only Look Once) algorithm, to detect crop types, assess plant health, and identify early symptoms of bacterial or viral infections. Supplementary software algorithms are implemented to analyze water levels, soil moisture, and nutrient deficiencies using image processing and spectral data analysis. In parallel, the same drone system functions as a rapid disaster response tool. It can monitor flood- prone regions, assess drought severity, and evaluate post-disaster damage to agricultural zones. The gathered data is processed in real time and made accessible to farmers, agricultural experts, and disaster management authorities via a centralized dashboard. Experimental results show high accuracy in disease prediction and anomaly detection, as well as efficiency in disaster impact assessment. This dual- purpose drone system not only enhances sustainable farming practices but also provides critical support during environmental emergencies, contributing to long-term agricultural security and community resilience.