Comparison of plant proximal sensing approaches for nitrogen supply detection in crops. (17th October 2022) - Rosso, Pablo; Wallor, Evelyn; Richter, Lars; Wehrhan, Marc
Abstract: Nondestructive proximal sensors can be an efficient source of information of N status in crops for localized and rapid adjustment of fertilization applications. The aim of this study was to compare two transmittance/reflectance‐based sensors (SPAD, ASD) and a florescence‐based sensor (Multiplex) in their ability to measure N content in corn ( Zea mays L.), spring and winter barley ( Hordeum vulgare L.), and rye ( Secale cereale L.), both at the leaf and canopy level. Measurements of leaves and canopies from six fertilization field trials in 2019 and 2020 were analyzed to establish relationships between sensor information and laboratory‐determined N content in crops. Analyses included linear regression for single sensor variables and machine learning for multivariate approaches, to assess the relative accuracy of the proximal sensors to measure N. The ASD is time‐intensive and requires post hoc analyses of the spectra. However, the spectral outputs of this device were clearly correlated with the N status of leaves and canopies. At the leaf level, SPAD showed higher accuracy than any of the single Multiplex variables to predict plant N. Multiplex performance could be improved by combining three of its variables. At the canopy level, interpolated SPAD values and the best‐performing Multiplex variables showed similar accuracy. It could be concluded that the relationship sensor‐N status is species specific. Despite the high standard deviation recorded in some raw Multiplex variable, the derived indices showed a comparable low standard deviation. At both, leaf and canopy levels an integrated sensor solution would combine the multidimensionality of Multiplex and ASD, and the accuracy and practicality of SPAD. Core Ideas: Results confirm crop‐specific relations between sensor signals and total N content. Results show clear differences in this relation between leaf and canopy level. To exploit the total output of Multiplex a multivariate approach is recommended. At leaf level SPAD reflects most accurately crop N status, but requires an active user involvement and knowledge. At canopy level SPAD and Multiplex seem similarly accurate, although Multiplex measures faster and more reliable.