IDENTIFICATION OF CROP HEALTH USING AI-ENABLED REMOTE SENSING

Abstract

The rapid advancement of remote sensing technology, combined with artificial intelligence (AI), has opened new avenues for precision agriculture, particularly in the identification of crop health. This paper explores the integration of AI algorithms with remote sensing techniques, enabling the accurate detection and diagnosis of crop health conditions in real-time. Remote sensing devices capture high-resolution data through satellite, UAV (unmanned aerial vehicle), and ground-based sensors, while AI processes this data to detect patterns associated with various crop health indicators, such as nutrient deficiencies, disease symptoms, water stress, and pest infestations. AI techniques, including machine learning (ML), deep learning, and computer vision, automate and enhance the interpretation of this extensive dataset. This approach reduces dependency on traditional, labour-intensive scouting methods and offers a cost-effective, scalable solution for monitoring crop health across large agricultural areas. The paper also discusses potential challenges while suggesting directions for future research.