Aerial Pharmacobotany

Abstract

The identification and extraction of medicinal plants in dense and remote forest regions present significant challenges due to the lack of accurate locational data, high human risk, and inefficiencies in manual plant recognition. This paper presents a drone-assisted aerial pharmacobotany system that integrates artificial intelligence (AI), deep learning, and autonomous navigation technologies to address these issues. By utilizing GPS-enabled drones equipped with high-resolution cameras and advanced image processing algorithms, the system autonomously captures, identifies, and maps medicinal plants in real time. The AI module compares captured images with a curated database to distinguish between medicinal and non-medicinal flora, ensuring precise classification. This approach minimizes biodiversity loss, reduces human exposure to hazardous environments, and enhances the accuracy and efficiency of plant identification. The system also contributes significantly to pharmaceutical research, conservation efforts, and sustainable resource extraction. Experimental evaluations and existing literature validate the potential of UAVs and AI in revolutionizing plant based medical discovery, making the proposed system a scalable and impactful solution for global forest ecosystems.