Precision horticulture represents a paradigm shift in fruit crop management by enabling site specific, data driven interventions that address the spatial and temporal variability inherent to perennial orchard systems. Uniform management practices are increasingly ineffective for fruit crops such as mango, apple, citrus, grape and banana, where heterogeneity in canopy structure, soil properties, and microclimate strongly influences productivity and resource use efficiency. Recent advances in digital agriculture particularly unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensor networks, artificial intelligence (AI), machine learning (ML) and integrated decision support systems (DSS) have substantially expanded the scope and scalability of precision fruit farming.

UAV-based remote sensing has emerged as a core technology due to its ability to generate high-resolution multispectral, hyperspectral, and thermal imagery for near real-time orchard diagnostics. Techniques such as vegetation index mapping (NDVI and NDRE), canopy vigour assessment, thermal stress detection, and 3D canopy reconstruction support precision irrigation in apple and grape, while AI-based image analytics enable early pest and disease detection in mango and citrus. These aerial insights, combined with IoT-based soil moisture, nutrient, and microclimate sensors, enable closed loop decision systems for optimized fertigation and yield forecasting.