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

Abstract

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.

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

“…The accuracy of some algorithms reached over 90% after being trained and tested, as seen in Figure 7 . This confirms the significance of using machine learning (ML), deep learning (DL), and data mining (DM) in AI tools for high-throughput pigment phenotyping screening of 11 lettuce varieties, as previously reported [ 1 , 18 , 27 , 28 ]. To enhance accuracy, it is recommended to combine higher concentrations of Chl, Car, AnC, Flv, and Phe in leaves with leaf thickness.…”

“…To enhance accuracy, it is recommended to combine higher concentrations of Chl, Car, AnC, Flv, and Phe in leaves with leaf thickness. In this sense, research, based on differences in biochemical and biophysical parameters, particularly in NIR-SWIR spectra, suggests that AI tools can effectively discriminate between lettuce varieties [ 1 , 18 , 29 ].…”

“…Lactuca sativa L., also known as lettuce, is a nutritionally important food for the population [ 1 , 2 ]. It is a popular and economically significant vegetable consumed worldwide, with an estimated production of 27 million tons according to the Food and Agriculture Organization (FAO) in 2022 [ 3 ].…”

“…This is of great importance, considering that the global food economy is estimated at approximately $750 billion annually, and AIAs can help reduce food waste and improve consumer satisfaction. Accordingly, the integration of AIAs and hyperspectral data mining, deep learning, and machine learning techniques promises to be a promising approach for crop plant classification [ 1 , 7 , 12 ].…”

“…This allows for advances in accuracy in monitoring crop development and growth [ 13 ], as well as other structural and water-related characteristics of leaves [ 9 , 13 , 15 ]. In this sense, the integration of AI algorithms for rapid classification and learning, encompassing morphological, genetic, biochemical, and physiological attributes, presents a new perspective in remote sensing in crop science [ 1 , 16 , 17 ]. Accordingly, the highest accuracy of the model can then be used to classify agronomically relevant varieties in large sample sets based on specific spectral signatures and AIAs linked to deconvolution wavelengths [ 3 , 18 , 19 , 20 ].…”