Evaluation of ground based spectral imaging for real time maize biomass monitoring

Andrea Szabó^1,2*Gift Siphiwe Nxumalo^1,2,3*Erika Buday-Bódi1^1,2Blessing Ademola⁴János Tamás^1,5Attila Nagy^1,2

• ¹1 Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 146B Böszörményi str., 4032 Debrecen, Hungary, Debrecen, Hungary
• ²2National Laboratory for Water Science and Water Safety, Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environment, Debrecen, Hungary
• ³University of Debrecen, Debrecen, Hungary
• ⁴Department of Biomedical, Biological and Chemical Engineering, College of Engineering, University of Missouri, Columbia, Missouri, United States
• ⁵2National Laboratory for Water Science and Water Safety, Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmen, Debrecen, Hungary

Although point measurements of water management properties have become increasingly common, understanding the spatial heterogeneity of agricultural fields remains critical for advancing precision agriculture. Spectral analysis provides a non-destructive approach to evaluating plant biophysical properties, such as chlorophyll and carotenoids, which are critical for precision agriculture. This study addresses the challenge of precise plant trait prediction by integrating proximal sensing data with biomass observations to inform more effective water management strategies. This study addresses the challenge of accurately predicting plant characteristics by integrating proximal sensing data with biomass measurements to enhance water management practices. This study predicts carotenoid and chlorophyll content from NDVI, and estimates dry and wet biomass from vegetation cover stress thresholds, plant-based indicators like the Plant Water Stress Index show promise ((Alharbi et al., 2024)Alharbi et al., 2024).Traditional biomass estimation methods are labour-intensive and limited in scale, leading to increased use of satellite, Unmanned Aerial Vehicle (UAV), and ground-based sensors. Vegetation indices derived from spectral reflectance data (e.g., Normalized Difference Vegetation Index (NDVI)) offer reliable insights into plant health and biomass.