Visible and near-infrared spectroscopy predicted leaf nitrogen contents of potato varieties under different growth and management conditions

Abstract

Visible-Near Infrared (vis-NIR) spectroscopy can provide a faster, cost-effective, and user-friendly solution to monitor leaf N status, potentially overcoming the limitations of current techniques. The objectives of the study were to develop and validate partial least square regression (PLSR) to estimate the total N contents of fresh and removed leaves of potatoes using the vis-NIR spectral range (350–2500 nm) generated from a handheld proximal sensor. The model was built using data collected from Hancock Agricultural Research Station, WI, USA in 2020 and was validated using samples collected in 2021 for four different conditions. The conditions included two sites (Coloma and Hancock), four potato varieties (Burbank, Norkotah, Goldrush, and Silverton), two N rates (unfertilized and 308 kg N ha −1 ), and four growth stages (vegetative, tuber initiation, tuber bulking, and tuber maturation). The calibration and validation models had high predictive performance for leaf total N with R 2 > 0.8 and RPD > 2. The model accuracy was affected by the total N contents in the leaf samples where the model underpredicted the samples with total leaf N contents greater than 6%.

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