Application of GIS and UAS for crop condition analysis in the Sofia region Application of GIS and UAS for crop condition analysis in the Sofia region Application of GIS and UAS for crop condition analysis in the Sofia region Application of GIS and UAS for crop condition analysis in the Sofia region

Abstract

The technological advancements in recent decades have facilitated the widespread adoption of unmanned aerial systems (UAS) and remote sensing methodologies in agricultural practices. These innovative approaches enable precise crop monitoring, early stress detection, and yield optimization through the analysis of vegetation indices derived from aerial imagery. This study investigates the application of UAS-acquired multispectral data and vegetation indices for crop health assessment, with particular emphasis on barley (Hordeum vulgare L.) and sunflower (Helianthus annuus L.). Geographic Information Systems (GIS) serve as a critical platform for integrating, processing, and visualizing remote sensing data, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), and Normalized Difference Red Index (NDRI). Our research methodology employed a DJI Mavic 3M UAS equipped with multispectral sensors to conduct aerial surveys of agricultural plots in the Sofia region of Bulgaria. The acquired data were processed to generate index maps that facilitate quantitative assessment of crop physiological status.The implemented workflow demonstrates an efficient technology for rapid identification of agronomic issues and supports data-driven decision making. The results highlight the potential of UAS-GIS integration for precision agriculture applications, particularly in monitoring cereal and oilseed crops under temperate climatic conditions. This approach provides agricultural stakeholders with timely, spatially explicit information for crop management while establishing a framework for future research in precision farming technologies.

Plant phenotyping relevance

UASマルチスペクトル画像と植生指数により作物の生理状態・健全性を定量評価する取得・解析ワークフローが研究の中心であり、単なる生物学的実験の routine 測定ではない。

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