Data collection and managing of remote sensing data for Urochloa spp. and Megathyrsus maximus breeding pipelines - Camelo, Rodrigo; Espitia Buitrago, Paula; Arboleda, Ronald; Jauregui, Rosa (2024)
Remote sensing has expanded opportunities to enhance genetic gain in breeding programs by increasing selection accuracy, reducing evaluation time, and enabling the incorporation of additional traits through association with multiple color indices (Araus et al., 2018). One of the main advances in recent years has been the integration of drone technologies into the evaluation of breeding trials, with multispectral cameras providing a significant advantage. Measuring near-infrared and red-edge bands allows for the calculation of vegetation indices that detect changes in chloroplast reflectance, offering valuable insights into plant health, photosynthetic efficiency, and stress responses. Chloroplast reflectance peaks in the near-infrared band (around 850 nm) and undergoes a rapid shift at the red-edge band (around 700 nm), making these wavelengths critical for precise vegetation monitoring and analysis (Guo et al., 2021). However, multispectral data acquisition and processing require not only highly skilled labor but also a robust framework for data management and an adequate cyber infrastructure to handle the storage, analysis, and accessibility of large datasets efficiently (Gano et al., 2024).