Unified exploratory data analysis system for potato breeding and monitoring using IoT and UAV data

Breeding programs increasingly require high-resolution, continuous monitoring to evaluate potato varieties under rapidly changing climatic conditions. However, existing Exploratory Data Analysis (EDA) tools rarely integrate heterogeneous data streams--Uncrewed Aerial Vehicles (UAV) imagery, Internet of Things (IoT) sensing, and field observations--into a unified, automated, and breeder-friendly workflow. This fragmentation limits breeders’ ability to detect stress, compare genotypes, and make timely, evidence-based decisions. We present a unified EDA system that consolidates multispectral UAV data, in-field IoT temperature measurements, historical agronomic records, and Geographical Information System (GIS) based spatial analyses into a single end-to-end architecture. The platform automates data acquisition, Extract, Transform, Load (ETL) processes, quality control, and relational storage while offering an interactive interface for flexible visualization and multi-scale analysis. The system was deployed over two growing seasons in Latvia using two potato varieties (Brasla and Imanta), 10 UAV missions per year, ~4,800 multispectral images per season, and continuous 5-min sensor data. Field evaluations with professional breeders show that the system enhances data exploration, improves usability, and reduces information loss compared with traditional manual workflows. A qualitative assessment of the interface indicated greater user confidence and clearer access to key indicators. To address the absence of established benchmarks, we provide quantitative metrics demonstrating data quality, precision, automation performance, and integration reliability. Comparisons of our Normalized Difference Vegetation Index (NDVI) outputs with values reported in the literature further validate the robustness of the multispectral processing pipeline. Overall, this work introduces the first unified, breeder-oriented EDA framework that combines high-precision monitoring, automated multi-source integration, and interactive analytics. The results demonstrate how accessible digital tools can accelerate varietal selection and support more resilient and sustainable potato breeding under increasing environmental variability.