Integrating Unmanned Aerial Vehicles and AI for Sustainable Tea Cultivation: A Focus on Early Disease Diagnosis

Raushan Kumar

Abstract: Tea farming faces significant challenges due to diseases that reduce yield and quality, posing threats to the sustainability of production systems. Integrating unmanned aerial vehicles (UAVs) with artificial intelligence (AI) offers transformative potential for early disease diagnosis, enabling precision management and sustainable practices in tea cultivation. This review explores state-of-the-art UAV platforms equipped with multispectral, hyperspectral, and thermal imaging sensors, which capture high-resolution data for disease detection. Advanced AI techniques, including machine learning and deep learning algorithms, process this data to identify disease patterns, severity, and spread with remarkable accuracy. Furthermore, the integration of UAVs and AI reduces reliance on chemical inputs by facilitating targeted interventions, contributing to environmental sustainability and cost efficiency. By analyzing existing studies and advancements, this review highlights the potential of UAVs and AI in transforming tea farming, addressing challenges, and providing actionable insights for future research in agricultural automation and disease management.

Keywords: Unmanned aerial vehicles (UAVs), Artificial intelligence (AI), Tea cultivation, Early disease detection. Precision agriculture, Remote sensing