Prediction of Corn Leaf Nitrogen Content in a Tropical Region Using Vis-NIR-SWIR Spectroscopy

Abstract

Traditional techniques for measuring leaf nitrogen content (LNC) involve slow and laborious processes, and radiometric data have been used to assist in the nutritional analysis of plants. Therefore, this study aimed to evaluate the performance of LNC predictions in corn plants based on laboratory hyperspectral Vis-NIR-SWIR data. The treatments corresponded to 60, 120, 180, and 240 kg ha−1 of nitrogen, in addition to the control (0 kg ha−1), and they were distributed using a randomized complete block design. At the laboratory, hyperspectral data of the leaves and LNC were obtained. The hyperspectral data were used in the calculation of different vegetation indices (VIs), which were applied in a predictive model—partial least squares regression (PLSR)—and the capacity of the prediction was assessed. The combination of bands and VIs generated a better prediction (0.74 < R2 < 0.87; 1.00 < RMSE < 1.50 kg ha−1) in comparison with the individual prediction by band (0.69 < R2 < 0.85; 1.00 < RMSE < 1.77 kg ha−1) and by VI (0.55 < R2 < 0.68; 1.00 < RMSE < 1.78 kg ha−1). Hyperspectral data offer a new opportunity to monitor the LNC in corn plants, especially in the region comprising the bands from 450 to 750 nm, since these were the bands that were most sensitive to the LNC.