{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T04:44:18Z","timestamp":1772772258406,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior, Brasil (CAPES)","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u20132500 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 &gt;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.<\/jats:p>","DOI":"10.3390\/rs14246330","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T03:01:51Z","timestamp":1671073311000},"page":"6330","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2343-5045","authenticated-orcid":false,"given":"Renan","family":"Falcioni","sequence":"first","affiliation":[{"name":"Graduate Program in Agronomy, Department of Agronomy, State University of Maring\u00e1, Av. Colombo, 5790, Maring\u00e1 87020-900, Paran\u00e1, Brazil"}]},{"given":"Jo\u00e3o Vitor Ferreira","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Graduate Program in Agronomy, Department of Agronomy, State University of Maring\u00e1, Av. Colombo, 5790, Maring\u00e1 87020-900, Paran\u00e1, Brazil"}]},{"given":"Karym Mayara de","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Graduate Program in Agronomy, Department of Agronomy, State University of Maring\u00e1, Av. Colombo, 5790, Maring\u00e1 87020-900, Paran\u00e1, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8430-2791","authenticated-orcid":false,"given":"Werner Camargos","family":"Antunes","sequence":"additional","affiliation":[{"name":"Graduate Program in Agronomy, Department of Agronomy, State University of Maring\u00e1, Av. Colombo, 5790, Maring\u00e1 87020-900, Paran\u00e1, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-2661","authenticated-orcid":false,"given":"Marcos Rafael","family":"Nanni","sequence":"additional","affiliation":[{"name":"Graduate Program in Agronomy, Department of Agronomy, State University of Maring\u00e1, Av. Colombo, 5790, Maring\u00e1 87020-900, Paran\u00e1, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Buturi, C.V., Sabatino, L., Mauro, R.P., Navarro-Le\u00f3n, E., Blasco, B., Leonardi, C., and Giuffrida, F. (2022). Iron Biofortification of Greenhouse Soilless Lettuce: An Effective Agronomic Tool to Improve the Dietary Mineral Intake. Agronomy, 12.","DOI":"10.3390\/agronomy12081793"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hong, J., Xu, F., Chen, G., Huang, X., Wang, S., Du, L., and Ding, G. (2022). 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