Interactive Hyperspectral Imaging Data Visualization: A Catalyst for Sustainable Horticultural Practices with Artificial Intelligence

This work investigates the revolutionary potential of AI-powered interactive visualisation for sustainable horticulture practises. The amount of spectrum data provided by hyperspectral photography on the Earth's surface enables the observation and analysis of various phenomena that are vital to environmentally sustainable land use, agriculture, and research. However, there are numerous obstacles in handling and analysing the massive amounts of complex data provided by hyperspectral sensors. To address these issues, our study culminated in the development of an AIdriven interactive visualisation system that enables users to rapidly extract relevant information from hyperspectral data. The framework delivers great dependability in the interpretation of complicated hyperspectral data, with outstanding recall, accuracy, precision, and F1 score values. Furthermore, based on our statistics, user engagement scores show how well the framework engages users. The framework's utility and applicability are proved by the effects it has on decision-making, user happiness, and visual clarity. Our research shows that combining AI and hyperspectral imaging can improve horticulture practises and encourage resource- and environmentally-friendly ways, which has substantial practical implications. By using this strategy, growers and environmental scientists can avoid resource waste, make datadriven decisions, and ameliorate the negative environmental effects of horticulture practises.