Nitrogen is one of the essential nutrients for the production of agricultural crops, participating in a complex interaction among soil, plant and the atmosphere. Therefore, its monitoring is important both economically and environmentally. The aim of this work was to estimate the leaf nitrogen contents in sugarcane from hyperspectral reflectance data during different vegetative stages of the plant. The assessments were performed from an experiment designed in completely randomized blocks, with increasing nitrogen doses (0, 60, 120 and 180 kg ha⁻¹). The acquisition of the spectral data occurred at different stages of crop development (67, 99, 144, 164, 200, 228, 255 and 313 days after cutting; DAC). In the laboratory, the hyperspectral responses of the leaves and the Leaf Nitrogen Contents (LNC) were obtained. The hyperspectral data and the LNC values were used to generate spectral models employing the technique of Partial Least Squares Regression (PLSR) Analysis, also with the calculation of the spectral bands of greatest relevance, by the Variable Importance in Projection (VIP). In general, the increase in LNC promoted a smaller reflectance in all wavelengths in the visible (400–680 nm). Acceptable models were obtained (R² > 0.70 and RMSE 0.81 and RMSE <1.24 g kg⁻¹. An independent validation, leave-one-date-out cross validation (LOOCV), was performed using data from other collections, which confirmed the robustness and the possibility of LNC prediction in new data sets, derived, for instance, from samplings subsequent to the period of study.

This study applied spectroradiometry techniques with hyperspectral data to identify the correlations between sugarcane leaf reflectance and the contents of Nitrogen (N), phosphorus (P), Potassium (K), Sulfur (S), Calcium (Ca) and Magnesium (Mg). During the harvests 2019/20 and 2020/21, sugarcane was introduced to nutritional stress by the application of limestone doses. Liming was applied in a fractional way and, at the end of five years, the amounts corresponded to 0, 9, 15 and 21 t ha^-1 of dolomitic limestone. The leaf hyperspectral reflectance data and the state of nutrients in the exponential growth phase of the culture were registered. The wavelengths correlated with N, P, K, S, Ca and Mg were identified using the Spearman’s correlation analysis. The test of similarity (ANOSIM) and the Principal Component Analysis (PCA) were applied to evaluate data variability, as well as the Partial Least Squares Regression (PLSR) for the prediction of the nutritional contents. The order of the degree of correlation in the region of visible was: P > K > N > Ca > S > Mg and for the region of the near infrared: P > K > Ca > N > S > Mg. P presented peaks with high correlations in the wavelengths 706-717 nm (-0.78) and 522-543 nm (-0.76). The values of the PLSR registered the best spectral responses in the region of VIS and red-edge, regions that are more sensitive to the deficiency of sulfur, potassium and phosphorus.

Beans are the main direct source of protein consumed by humans in the world and their productivity is directly linked to nitrogen. The short crop cycle imposes the need for fast methodologies for N quantification. In this work, we evaluated the performance of four machine learning algorithms in nitrogen estimation using NIR spectroscopy, comparing predictions between complete spectral data and only intervals obtained with the variable importance in projection (VIP). Doses of 0, 50, 100, and 150 kg ha⁻¹ of N were applied and leaf reflectance was collected. Weka software was used to test the algorithms. The selection of the most effective spectral zones was made with the variable importance in projection (VIP). The intervals of 700–740 nm and 983–995 nm were considered the most important for the study of nitrogen. More efficient predictions were verified for RF and KNN models (R² = 0.89, RMSE = 2.23 g kg⁻¹; and R² = 0.80, RMSE = 2.89 g kg⁻¹, respectively) when only the most important spectral regions were included. The efficiency of nitrogen prediction based on NIR reflectance combined with machine learning was verified, which can serve as an important tool in precision agriculture.

Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted in three regions of São Paulo, Brazil (Jaú, Piracicaba and Santa Maria), the research involved three experiments, one per location. The spectral data were obtained at 140, 170, 200, 230 and 260 days after cutting (DAC). From the hyperspectral data, clustering analysis was performed to identify the patterns between the spectral bands for each region where the spectral readings were made, using the Partitioning Around Medoids (PAM) algorithm. Then, the LNC values were used to generate spectral models using Partial Least Squares Regression (PLSR). Subsequently, the generalization of the models was tested with the leave-one-date-out cross-validation (LOOCV) technique. The results showed that although the variation in leaf N was small, the sensor demonstrated the ability to detect these variations. Furthermore, it was possible to determine the influence of N concentrations on the leaf spectra and how this impacted cluster formation. It was observed that the greater the average variation in N content in each cluster, the better defined and denser the groups formed were. The best time to quantify N concentrations was at 140 DAC (R² = 0.90 and RMSE = 0.74 g kg⁻¹). From LOOCV, the areas with sandier soil texture presented a lower model performance compared to areas with clayey soil, with R² < 0.54. The spatial generalization of the models recorded the best performance at 140 DAC (R² = 0.69, RMSE = 1.18 g kg⁻¹ and dr = 0.61), decreasing in accuracy at the crop-maturation stage (260 DAC), R² of 0.05, RMSE of 1.73 g kg⁻¹ and dr of 0.38. Although the technique needs further studies to be improved, our results demonstrated potential, which tends to provide support and benefits for the quantification of nutrients in sugarcane in the long term.

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.