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

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 fi...

Full description

Bibliographic Details
Main Authors: Renan Falcioni, João Vitor Ferreira Gonçalves, Karym Mayara de Oliveira, Werner Camargos Antunes, Marcos Rafael Nanni
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/14/24/6330
_version_ 1797455460301799424
author Renan Falcioni
João Vitor Ferreira Gonçalves
Karym Mayara de Oliveira
Werner Camargos Antunes
Marcos Rafael Nanni
author_facet Renan Falcioni
João Vitor Ferreira Gonçalves
Karym Mayara de Oliveira
Werner Camargos Antunes
Marcos Rafael Nanni
author_sort Renan Falcioni
collection DOAJ
description 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 R<sup>2</sup><sub>P</sub> and RMSE<sub>P</sub> 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.
first_indexed 2024-03-09T15:53:45Z
format Article
id doaj.art-9be7bb0f9e384c679c8aacaf77608911
institution Directory Open Access Journal
issn 2072-4292
language English
last_indexed 2024-03-09T15:53:45Z
publishDate 2022-12-01
publisher MDPI AG
record_format Article
series Remote Sensing
spelling doaj.art-9be7bb0f9e384c679c8aacaf776089112023-11-24T17:47:58ZengMDPI AGRemote Sensing2072-42922022-12-011424633010.3390/rs14246330VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green LettuceRenan Falcioni0João Vitor Ferreira Gonçalves1Karym Mayara de Oliveira2Werner Camargos Antunes3Marcos Rafael Nanni4Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, BrazilGraduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, BrazilGraduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, BrazilGraduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, BrazilGraduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, BrazilVIS-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 R<sup>2</sup><sub>P</sub> and RMSE<sub>P</sub> 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.https://www.mdpi.com/2072-4292/14/24/6330absorbancefingerprintleaf thicknessmultivariate analysisPLSR analysisprecision agriculture
spellingShingle Renan Falcioni
João Vitor Ferreira Gonçalves
Karym Mayara de Oliveira
Werner Camargos Antunes
Marcos Rafael Nanni
VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce
Remote Sensing
absorbance
fingerprint
leaf thickness
multivariate analysis
PLSR analysis
precision agriculture
title VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce
title_full VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce
title_fullStr VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce
title_full_unstemmed VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce
title_short VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce
title_sort vis nir swir hyperspectroscopy combined with data mining and machine learning for classification of predicted chemometrics of green lettuce
topic absorbance
fingerprint
leaf thickness
multivariate analysis
PLSR analysis
precision agriculture
url https://www.mdpi.com/2072-4292/14/24/6330
work_keys_str_mv AT renanfalcioni visnirswirhyperspectroscopycombinedwithdataminingandmachinelearningforclassificationofpredictedchemometricsofgreenlettuce
AT joaovitorferreiragoncalves visnirswirhyperspectroscopycombinedwithdataminingandmachinelearningforclassificationofpredictedchemometricsofgreenlettuce
AT karymmayaradeoliveira visnirswirhyperspectroscopycombinedwithdataminingandmachinelearningforclassificationofpredictedchemometricsofgreenlettuce
AT wernercamargosantunes visnirswirhyperspectroscopycombinedwithdataminingandmachinelearningforclassificationofpredictedchemometricsofgreenlettuce
AT marcosrafaelnanni visnirswirhyperspectroscopycombinedwithdataminingandmachinelearningforclassificationofpredictedchemometricsofgreenlettuce