Application of hyperspectral remote sensing, GIS, GPS and artificial intelligence in floriculture and horticulture

Abstract

Floriculture and horticulture are rapidly evolving toward technology-driven production systems to meet the increasing global demand for quality, sustainability, and profitability. Recent advances in Hyperspectral Remote Sensing (HRS), Geographic Information Systems (GIS), Global Positioning System (GPS), and Artificial Intelligence (AI) have revolutionized monitoring, management, and decision-making in crop production. This review consolidates recent developments in the application of these tools to ornamental and horticultural crops, emphasizing their integration within GeoAI frameworks for real-time, spatially explicit analysis. HRS enables early detection of nutrient deficiencies, pest or disease onset, and pigment variations that directly affect flower colour and market value. GIS and GPS provide spatial precision for input management, yield mapping, and post-harvest logistics, while AI-based models automate classification, grading, and stress prediction with high accuracy. The combined use of these technologies enhances productivity, optimizes resource utilization, and supports climate-smart, sustainable cultivation practices. However, challenges such as high equipment cost, limited spectral databases, data-processing complexity, and the need for skilled personnel constrain large-scale adoption. The review identifies emerging research priorities, including low-cost sensor development, explainable AI, multisensory data fusion, and standardized spectral repositories for floriculture and horticulture. Ultimately, the synergistic use of HRS-GIS-GPS-AI can transform traditional cultivation into smart, resilient, and data-driven production systems, supporting both economic growth and environmental stewardship in the ornamental and allied horticultural sectors.