VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-12, Vol.14 (24), p.6330
• Main Authors: Falcioni, Renan, Gonçalves, João Vitor Ferreira, Oliveira, Karym Mayara de, Antunes, Werner Camargos, Nanni, Marcos Rafael
• Format: Article
• Language: English
• Subjects: Absorbance; Accuracy; Agricultural production; Algorithms; Carotenoids; Chemometrics; Classification; Crop management; Crop yield; Data mining; Discriminant analysis; Farming systems; fingerprint; Fingerprints; Functional groups; leaf thickness; Learning algorithms; Lettuce; Light; Machine learning; multivariate analysis; Near infrared radiation; Parameters; Plants (botany); PLSR analysis; precision agriculture; Precision farming; Principal components analysis; Reflectance; Remote sensing; Short wave radiation; Software; Spectroradiometers; Spectroscopy; Spectrum analysis; Statistical analysis; Support vector machines; Thickness; Transmittance; Variance analysis; Vegetables
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14246330

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.