Integrating plant morphological traits with remote-sensed multispectral imageries for accurate corn grain yield prediction

• Published in: PloS one Vol. 19; no. 4; p. e0297027
• Main Authors: Jang, Chunhwa, Namoi, Nictor, Wolske, Eric, Wasonga, Daniel, Behnke, Gevan, Bowman, N Dennis, Lee, D K
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.04.2024; Public Library of Science (PLoS)
• Subjects: Ammonium compounds; Ammonium paratungstate; Analysis; Biology and Life Sciences; Botany; Chlorophyll; Corn; Crop yields; Cropping systems; Drone aircraft; Edible Grain; Engineering and Technology; Fertilizers; Methods; Morphology; Physical Sciences; Remote sensing; Remote Sensing Technology - methods; Research and Analysis Methods; Urea; Zea mays; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0297027

Sustainable crop production requires adequate and efficient management practices to reduce the negative environmental impacts of excessive nitrogen (N) fertilization. Remote sensing has gained traction as a low-cost and time-efficient tool for monitoring and managing cropping systems. In this study, vegetation indices (VIs) obtained from an unmanned aerial vehicle (UAV) were used to detect corn (Zea mays L.) response to varying N rates (ranging from 0 to 208 kg N ha-1) and fertilizer application methods (liquid urea ammonium nitrate (UAN), urea side-dressing and slow-release fertilizer). Four VIs were evaluated at three different growth stages of corn (V6, R3, and physiological maturity) along with morphological traits including plant height and leaf chlorophyll content (SPAD) to determine their predictive capability for corn yield. Our results show no differences in grain yield (average 13.2 Mg ha-1) between furrow-applied slow-release fertilizer at ≥156 kg N ha-1 and 208 kg N ha-1 side-dressed urea. Early season remote-sensed VIs and morphological data collected at V6 were least effective for grain yield prediction. Moreover, multivariate grain yield prediction was more accurate than univariate. Late-season measurements at the R3 and mature growth stages using a combination of normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) in a multilinear regression model showed effective prediction for corn yield. Additionally, a combination of NDVI and normalized difference red edge index (NDRE) in a multi-exponential regression model also demonstrated good prediction capabilities.