AI-Driven Horticulture: Interactive Hyperspectral Data Visualization

The integration of advanced artificial intelligence (AI) techniques into horticulture has opened new avenues for optimizing crop management and enhancing productivity. This study explores the application of K-means clustering and Generative Adversarial Networks (GANs) in horticultural practices, focusing on interactive hyperspectral data visualization. Through the evaluation of K-means clustering for plant health assessment, precision values ranging from 0.82 to 0.87 and recall values ranging from 0.77 to 0.84 were observed across 10 experimental trials, affirming the algorithm's efficacy in accurately classifying plant health status. Additionally, GANs were employed to generate synthetic hyperspectral data, yielding structural similarity index (SSI) scores ranging from 0.90 to 0.94 and root mean square error (RMSE) values ranging from 0.03 to 0.07, underscoring the high fidelity of synthetic data compared to real-world observations. These results highlight the potential of AI-enhanced horticulture to revolutionize decision-making processes and resource management strategies. By leveraging AI techniques for spectral analysis and data synthesis, horticulturists can gain actionable insights into plant health, nutrient levels, and environmental conditions, leading to improved crop yields and sustainable agricultural practices.