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ABSTRACT: One of the causes of productivity loss in sugarcane cultivation has been associated with the species Diatraea saccharalis, also known as the sugarcane borer. Therefore, this study evaluated the feasibility of using hyperspectral sensors to obtain the leaf spectral response of sugarcane in different periods of infestation at leaf and canopy levels to diagnose D. saccharalis damage in advance. The study included three varieties of sugarcane: CTC9003BT, CTC4, and RB966928. The insecticide Altacor® was used to control pestsin half of the plots. Data collection occurred at the following stages: sprouting, tillering, stalke longation, and early maturation. Data regarding relative water content (RWC), leaf spectral signature in the laboratory and canopy, vegetation indices (NDVI and MCARI), productivity, purity, and total recoverable sugar (TRS) were collected to evaluate the borer effects on the sugarcane crop. The highest RWC was observed for CTC9003BT (60.15%) without insecticide and 59.0% with insecticide. The visible (400-680 nm) and near-infrared (750-1300 nm) spectral bands identified spectral variations in plants with and without sugarcane borer. The percentage of sugarcane borer showed high and negative correlations between productivity, NDVI, and TRS, with coefficients of -0.68, -0.76, and -0.76, respectively. The NDVI and MCARI indices effectively detect plants under stress, but their variation is influenced by multiple factors, making it difficult to associate them with a single problem (sugarcane borer).

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.

ABSTRACT: It is known that floriculture is very important for the world economic scenario. Nevertheless, the vase life of cut flowers is determined by a short time span. This study evaluated the spectral behavior during leaf degradation of lisianthus (Eustoma grandiflorum) flower stems in the postharvest, in different preservative solutions, and estimating its leaf pigments by hyperspectral data. Lisianthus floral stems were subjected to preservative solutions with different concentrations of sucrose, glucose, phytohormones and deionized water. Leaves from these stems were collected every 4 days for further hyperspectral analysis. Spectra was determined in laboratory with a sensor collecting at the range of 350-2500 nm. In order to analyze the use of the spectra to detect the performance of the preservative solutions, statistical tests were used at each wavelength. Pigment prediction was assessed by the CAR/CLF ratio (Ratio between Carotenoids and Chlorophyll) from the spectral curves, using PLSR and RRMSE. The glucose-based solutions registered positive effects on the preservation of floral stems, especially at the dose of 180 g/L. The solutions based on sucrose and phytohormones registered similarities in the spectral curves among the doses, demonstrating equality in leaf preservation. The discriminant analyses demonstrated there was statistical difference in the spectral responses in the doses for each solution used. The CAR/CLF ratio had reasonable coefficients of 0.6 and RRMSE below 6.99%. The hyperspectral analyses presented a potential for the evaluation of leaf degradation in lisianthus floral stems in different pulsing solutions used in the postharvest.

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

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.