Title : The use of spectral measurements in experiments and agricultural production
Abstract:
In recent years, the agricultural sector in Poland has experienced a technological revolution, largely attributable to the implementation of unmanned vehicle systems. These unmanned aerial (UAVs) and ground (UGVs) vehicles, outfitted with sophisticated multi-purpose systems, are fundamentally transforming crop management paradigms. Their applications range from terrestrial cultivation operations to aerial tasks integral to precision agriculture, such as high-resolution field mapping, targeted spraying, and variable-rate fertilizer distribution. The evolution of drone technology offers novel opportunities for the optimization of agronomic processes. UAVs are commonly equipped with advanced geospatial mapping systems, facilitating the generation of detailed topographic models and the analysis of multispectral or hyperspectral data. By employing specialized sensors and cameras, these platforms can acquire critical data on various crop biophysical parameters, including vegetation indices, canopy density, physiological health, and stress levels. Furthermore, these data acquisition systems allow for seamless integration with Geographic Information Systems (GIS) and other analytical software utilizing machine learning algorithms. The geospatial data collected can be leveraged to create highly customized, site-specific fertilization prescriptions. These prescriptions are informed by detailed soil analysis—encompassing composition, moisture content, pH, and other edaphic factors—and can be refined using crop health maps derived from spectral imagery, which enable the early detection and mapping of disease outbreaks or pest infestations. The acquisition of high-resolution spectral imagery, performed in a non-destructive manner, enabled a precise and multifaceted assessment of key agricultural crops. This analysis extended beyond evaluating plant health status and determining fertilization requirements to include the observation of other critical factors impacting final yield, such as soil heterogeneity and the estimation of soil moisture content. Furthermore, imagery acquired via unmanned aerial vehicles (UAVs) facilitated a more accurate delineation of drought-affected areas within a specific study plot when compared to traditional, costly methods reliant on in-situ measurements. The appropriate combination of spectral bands allowed for the calculation of the Normalized Difference Vegetation Index (NDVI). This index was instrumental for conducting both quantitative and qualitative analyses of vegetation status, which in turn provided a basis for monitoring dynamic changes within the crops throughout the growing season.
