VIS-NIR-SWIR hyperspectroscopy is a significant technique used in remote sensing for classification of prediction-based chemometrics and machine learning. Chemometrics, together with biophysical and biochemical parameters, is a laborious technique; however, researchers are very interested in this field because of the benefits in terms of optimizing crop yields. In this study, we investigated the hypothesis that VIS-NIR-SWIR could be efficiently applied for classification and prediction of leaf thickness and pigment profiling of green lettuce in terms of reflectance, transmittance, and absorbance data according to the variety. For this purpose, we used a spectroradiometer in the visible, near-infrared, and shortwave ranges (VIS-NIR-SWIR). The results showed many chemometric parameters and fingerprints in the 400–2500 nm spectral curve range. Therefore, this technique, combined with rapid data mining, machine learning algorithms, and other multivariate statistical analyses such as PCA, MCR, LDA, SVM, KNN, and PLSR, can be used as a tool to classify plants with the highest accuracy and precision. The fingerprints of the hyperspectral data indicated the presence of functional groups associated with biophysical and biochemical components in green lettuce, allowing the plants to be correctly classified with higher accuracy (99 to 100%). Biophysical parameters such as thickness could be predicted using PLSR models, which showed R2P and RMSEP values greater than >0.991 and 6.21, respectively, according to the relationship between absorbance and reflectance or transmittance spectroscopy curves. Thus, we report the methodology and confirm the ability of VIS-NIR-SWIR hyperspectroscopy to simultaneously classify and predict data with high accuracy and precision, at low cost and with rapid acquisition, based on a remote sensing tool, which can enable the successful management of crops such as green lettuce and other plants using precision agriculture systems.

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Visible (V), Near Infrared (NIR) and Short Waves Infrared (SWIR) spectroscopy has been indicated as a promising tool in soil studies, especially in the last decade. However, in order to apply this method, it is necessary to develop prediction models with the capacity to capture the intrinsic differences between agricultural areas and incorporate them in the modeling process. High quality estimates are generally obtained when these models are applied to soil samples displaying characteristics similar to the samples used in their construction. However, low quality predictions are noted when applied to samples from new areas presenting different characteristics. One way to solve this problem is by recalibrating the models using selected samples from the area of interest. Based on this premise, the aim of this study was to use the spiking technique and spiking associated with hybridization to expand prediction models and estimate organic matter content in a target area undergoing different uses and management. A total of 425 soil samples were used for the generation of the state model, as well as 200 samples from a target area to select the subsets (10 samples) used for model recalibration. The spectral readings of the samples were obtained in the laboratory using the ASD FieldSpec 3 Jr. Sensor from 350 to 2500 nm. The spectral curves of the samples were then associated to the soil attributes by means of a partial least squares regression (PLSR). The state model obtained better results when recalibrated with samples selected through a cluster analysis. The use of hybrid spectral curves did not generate significant improvements, presenting estimates, in most cases, lower than the state model applied without recalibration. The use of the isolated spiking technique was more effective in comparison with the spiked and hybridized state models, reaching r2, square root of mean prediction error (RMSEP) and ratio of performance to deviation (RPD) values of 0.43, 4.4 g dm−3, and 1.36, respectively.

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