Harnessing Hyperspectral Data Through Machine Learning and Deep Learning for Optimal Plant Health
摘要
Agriculture today grapples with the dual imperative of maximizing plant health and navigating environmental and economic considerations. The research innovations in sensor technologies have propelled us into a paradigm where hyperspectral data provides profound, granular insights into plant well-being. It emphasizes the game-changing confluence of integrating this richness with the sharp analytical capabilities of machine learning (ML) and deep learning to revolutionize plant health diagnostics. This manuscript comprehensively explores the diverse sensors employed in agriculture, how these techniques can add more depth and clarity to agriculture data, and unveils the fusion techniques vital for their symbiotic integration. Through illustrative case studies, we empathise with the benefits and insights gleaned from hyperspectral data and ML. Gazing into the future, the research delves into the unfolding horizons of tech advancements in agriculture.