We evaluated the use of airborne hyperspectral imaging and non-imaging sensors in the Vis—NIR—SWIR spectral region to assess particle size and soil organic matter in the surface layer of tropical soils (Oxisols, Ultisols, Entisols). The study area is near Piracicaba municipality, São Paulo state, Brazil, in a sugarcane cultivation area of 135 hectares. The study area, with bare soil, was imaged in April 2016 by the AisaFENIX aerotransported hyperspectral sensor, with spectral resolution of 3.5 nm between 380 and 970 nm, and 12 nm between 970 and 2500 nm. We collected 66 surface soil samples. The samples were analyzed for particle size and soil organic matter content. Laboratory spectral measurements were performed using a non-imaging spectroradiometer (ASD FieldSpec 3 Jr). Partial Least Square Regression (PLSR) was used to predict clay, silt, sand and soil organic matter (SOM). The PLSR functions developed were applied to the hyperspectral image of the study area, allowing development of a prediction map of clay, sand, and SOM. The developed PLSR models demonstrated the relationship between the predictor variables at the cross-validation step, both for the non-imaging and imaging sensors, when the highest r and R² values were obtained for clay, sand, and SOM, with R² over 0.67. We did not obtain a satisfactory model for silt content. For the non-imaging sensor at the prediction step, R² values for clay and SOM were over 0.7 and sand was lower than 0.54. The imaging sensor yielded models for clay, sand, and SOM with R² values of 0.62, 0.66, and 0.67, respectively. Pearson correlation between sensors was greater than 0.849 for the prediction of clay, sand, and SOM. Our study successfully generated, from the imaging sensor, a large-scale and detailed predicted soil maps for particle size and SOM, which are important in the management of tropical soils.

Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV−VIS−NIR−SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA₃) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA₃ concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R²_CV values ranging from 0.81 to 0.87 and RPD_P values exceeding 2.09 for all parameters. Based on Pearson’s coefficient XYZ interpolations and HVI algorithms, the NIR−SWIR band combination proved the most effective for predicting height and leaf area, while VIS−NIR was optimal for optimal energy yield, and VIS−VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s−PLS were most significant for SWIR1 and SWIR2, while i−PLS showed a more uniform distribution in VIS−NIR−SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.

Remote sensing techniques have the potential to monitor physiological and biochemical changes caused by nutritional stress in plants. However, few studies have monitored the influence of foliar phosphorus levels on the spectral responses of plants, especially maize. This work aimed to assess phosphorus nutritional status in maize plants subjected to different sources and doses of phosphate fertilizer using leaf-based hyperspectral measurements and multivariate analysis. The specific goals addressed were a) the qualitative analysis of the spectral response, b) the identification of significant spectral wavelengths for differentiating doses of phosphate fertilizer, and c) the development of a spectral model for classifying the nutritional status of maize as an effective approach for crop nutritional assessment. Two experiments with different sources and doses of phosphate fertilizer were carried out: experiment 1 with monoammonium phosphate fertilizer (MAP) and experiment 2 with simple superphosphate fertilizer (SS). Hyperspectral reflectance (visible and near-infrared wavelengths) was collected at the V6 development stage of maize using an ASD Fieldspec 3 Jr Spectroradiometer, totalling 100 samples for each experiment. The collected data were submitted to principal component analysis (PCA) and linear discriminant analysis (LDA). The spectral signature of leaves showed higher reflectance values in the Vis spectral interval and lower reflectance in NIR under the absence of fertilization. For the other treatments, smaller differences in their spectral response were observed, especially for experiment 2. For experiment 1, the LDA achieved accuracy from 46 to 72% in the cross-validation step and from 50 to 75% in the external validation step. For experiment 2, the accuracy ranged from 25 to 82% in the cross-validation step and from 28 to 85% in the external validation step. In general, most misclassification was observed among treatments with similar fertilizer doses. These results suggest the potential of using hyperspectral data to monitor leaf P levels in maize, which contributes to the determination of crop management zones based on production variability under P limitation, in addition to optimizing the use of chemical inputs and guaranteeing greater sustainability of production systems.

This study aimed to evaluate the use of spectroradiometry for the determination of soil exchangeable ions after the application of mining coproducts. The treatments were arranged in a double factorial scheme (7 × 2) with complete randomization and ten repetitions, and placed in pots in a greenhouse. The factors evaluated were the input type and soil class (Arenosol and Ferralsol). The inputs were limestone mining coproducts, which were applied in isolation and in blend. After 140 days, exchangeable ions and soil spectra were determined. The application of the coproducts increased the exchangeable ion contents: K⁺, Mg²⁺, Ca²⁺, P, SO₄²⁻ and Si. Moreover, estimation of the attributes through spectroradiometry showed satisfactory results for the Ca²⁺, K⁺ (resin and mehlich-1), P and Si from the soil, with R² in the prediction phase equal to 0.89, 0.96, 0.87, 0.71 and 0.92. For Mg²⁺ and SO₄²⁻, R² values were 0.44 and 0.12. Therefore, we conclude, under the conditions described in this paper, that spectroradiometry has the potential to determine some exchangeable ions, similar to routine soil fertility analyses.

Thousands of chemical analyses are carried out annually with the aim of recommending soil correction; however, these analyses are expensive, destructive, time-consuming, and can be harmful to the environment. As an alternative to conventional analysis methods, diffuse reflectance spectroscopy has been proposed as an option for evaluating the chemical characteristics of soil. The selection of variables has also emerged as an alternative to improve the performance of PLSR (partial least squares regression), as it decreases the root mean square error (RMSE) and increases the accuracy of the models. However, few studies have used a previous selection of variables for the construction of PLSR models to estimate the chemical characteristics of soil. In this context, the hypothesis in this study was that it is possible to calculate the liming recommendation in Oxisol based on the chemical characteristics estimated by PLSR, with a previous selection of variables using iPLS (Interval PLS). The objective was to calculate the need for liming based on chemical characteristics estimated via iPLS selection and PLSR modeling of specific wavelengths of soil reflectance. The experimental area was treated with different application rates of limestone, with and without incorporation, and phosphogypsum was applied in additional treatments. Soil assessments were carried out 5, 12, 24, and 36 months after the application of the treatments, using six layers: 0.00–0.05, 0.05–0.10, 0.10–0.20, 0.20–0.30, 0.30–0.40 and 0.40–0.60 m. Samples were subjected to conventional laboratory analyses, and spectral readings (400–2500 nm) were obtained with a spectroradiometer. The spectral curves were subjected to the iPLS variable selection method to generate PLSR models of the chemical characteristics used to calculate the liming recommendation. The chemical characteristics of the soil, such as Ca²⁺, sum of bases (SB), effective cation exchange capacity (CTCe), cation exchange capacity (CTC), and base saturation (BS), could be estimated, with values of R² ranging from 0.83 to 0.92 in the calibration and validation steps, and from 0.84 to 0.90 for the prediction step (in the fourth assessment). The liming recommendation calculated based on the chemical characteristics predicted from the PLSR models showed a strong correlation (r > 0.86) with the liming recommendation calculated by conventional laboratory techniques. The fourth soil assessment yielded the best correlation coefficient (r = 0.95).