Comparison of plant proximal sensing approaches for nitrogen supply detection in crops

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.

Plant phenotyping relevance

作物の窒素状態という植物生理形質を対象に、複数の近接セン​​サーの測定精度を比較・評価し、センサー情報と実験室測定値の関係を解析しているため、センシング手法の技術評価が中心である。

Code and data availability

The article describes Multiplex/SPAD/ASD proximal sensing measurements and regression/ML analyses for crop nitrogen detection, but no public phenotype dataset, sensor data, analysis code, or trained model is deposited. Supporting Information is mentioned only generically without a URL or deposit statement, and the I4S/

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