Hyperspectral imaging for monitoring agricultural crop imagery captured with drones

Abstract

Monitoring the health of agricultural crops is a critical task for ensuring food security and improving the efficiency of agricultural production. One of the most promising methods of monitoring is the use of hyperspectral imaging, which is carried out using drones. This technology allows for the acquisition of detailed information about the condition of crops, necessary for early disease detection and optimization of agricultural practices. The techniques for acquiring hyperspectral images can be classified into four types: point-by-point scanning, line-by-line scanning, wavelength scanning, and snapshot imaging. The aim is to study and process multispectral images of aquatic areas and agricultural crops for the operational assessment of crop health and yield prediction. The methodology includes an analysis of scientific literature on this topic and data processing in the Sentinel-2 program by determining the NDVI (Normalized Difference Vegetation Index). The main result of the research is a graphical representation of the images processed in the program, determining the condition of the crops, which leads to the conclusion that the application of hyperspectral imaging makes the agricultural process more efficient, faster, and more stable.