{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T11:17:17Z","timestamp":1770635837470,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T00:00:00Z","timestamp":1574726400000},"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","doi-asserted-by":"publisher","award":["88881.311850\/2018-01"],"award-info":[{"award-number":["88881.311850\/2018-01"]}],"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>Modeling the hyperspectral response of vegetables is important for estimating water stress through a noninvasive approach. This article evaluates the hyperspectral response of water-stress induced lettuce (Lactuca sativa L.) using artificial neural networks (ANN). We evenly split 36 lettuce pots into three groups: control, stress, and bacteria. Hyperspectral response was measured four times, during 14 days of stress induction, with an ASD Fieldspec HandHeld spectroradiometer (325\u20131075 nm). Both reflectance and absorbance measurements were calculated. Different biophysical parameters were also evaluated. The performance of the ANN approach was compared against other machine learning algorithms. Our results show that the ANN approach could separate the water-stressed lettuce from the non-stressed group with up to 80% accuracy at the beginning of the experiment. Additionally, this accuracy improved at the end of the experiment, reaching an accuracy of up to 93%. Absorbance data offered better accuracy than reflectance data to model it. This study demonstrated that it is possible to detect early stages of water stress in lettuce plants with high accuracy based on an ANN approach applied to hyperspectral data. The methodology has the potential to be applied to other species and cultivars in agricultural fields.<\/jats:p>","DOI":"10.3390\/rs11232797","type":"journal-article","created":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T10:57:27Z","timestamp":1574765847000},"page":"2797","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0258-536X","authenticated-orcid":false,"given":"Lucas Prado","family":"Osco","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Architecture, and Urbanism and Geography, Federal University of Mato Grosso do Sul, Av. Costa e Silva, Campo Grande 79070-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6633-2903","authenticated-orcid":false,"given":"Ana Paula Marques","family":"Ramos","sequence":"additional","affiliation":[{"name":"Environmental and Regional Development, University of Western S\u00e3o Paulo, R. Jos\u00e9 Bongiovani, 700-Cidade Universit\u00e1ria, Presidente Prudente 19050-920, Brazil"}]},{"given":"\u00c9rika Akemi Saito","family":"Moriya","sequence":"additional","affiliation":[{"name":"Department of Cartographic Science, S\u00e3o Paulo State University, Presidente Prudente 19060-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6590-6733","authenticated-orcid":false,"given":"Lorrayne Guimar\u00e3es","family":"Bavaresco","sequence":"additional","affiliation":[{"name":"Agronomy Development, University of Western S\u00e3o Paulo, R. Jos\u00e9 Bongiovani, 700-Cidade Universit\u00e1ria, Presidente Prudente 19050-920, Brazil"}]},{"given":"Bruna Coelho de","family":"Lima","sequence":"additional","affiliation":[{"name":"Agronomy Development, University of Western S\u00e3o Paulo, R. Jos\u00e9 Bongiovani, 700-Cidade Universit\u00e1ria, Presidente Prudente 19050-920, Brazil"}]},{"given":"Nayara","family":"Estrabis","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture, and Urbanism and Geography, Federal University of Mato Grosso do Sul, Av. Costa e Silva, Campo Grande 79070-900, Brazil"}]},{"given":"Danilo Roberto","family":"Pereira","sequence":"additional","affiliation":[{"name":"Environmental and Regional Development, University of Western S\u00e3o Paulo, R. Jos\u00e9 Bongiovani, 700-Cidade Universit\u00e1ria, Presidente Prudente 19050-920, Brazil"}]},{"given":"Jos\u00e9 Eduardo","family":"Creste","sequence":"additional","affiliation":[{"name":"Environmental and Regional Development, University of Western S\u00e3o Paulo, R. Jos\u00e9 Bongiovani, 700-Cidade Universit\u00e1ria, Presidente Prudente 19050-920, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9096-6866","authenticated-orcid":false,"given":"Jos\u00e9 Marcato","family":"J\u00fanior","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture, and Urbanism and Geography, Federal University of Mato Grosso do Sul, Av. Costa e Silva, Campo Grande 79070-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8815-6653","authenticated-orcid":false,"given":"Wesley Nunes","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture, and Urbanism and Geography, Federal University of Mato Grosso do Sul, Av. Costa e Silva, Campo Grande 79070-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0516-0567","authenticated-orcid":false,"given":"Nilton Nobuhiro","family":"Imai","sequence":"additional","affiliation":[{"name":"Department of Cartographic Science, S\u00e3o Paulo State University, Presidente Prudente 19060-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7899-0049","authenticated-orcid":false,"given":"Jonathan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Management and Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0564-7818","authenticated-orcid":false,"given":"Veraldo","family":"Liesenberg","sequence":"additional","affiliation":[{"name":"Forest Engineering Department, State University of Santa Catarina, R. Eng. Agron\u00f4mico Andrei Cristian Ferreira, Trindade, Florian\u00f3polis-SC 88040-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4614-9260","authenticated-orcid":false,"given":"F\u00e1bio Fernando de","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"Agronomy Development, University of Western S\u00e3o Paulo, R. Jos\u00e9 Bongiovani, 700-Cidade Universit\u00e1ria, Presidente Prudente 19050-920, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4213","DOI":"10.3390\/rs70404213","article-title":"High-resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: Application in breeding trials","volume":"7","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ampatzidis, Y., and Partel, V. (2019). UAV-based high throughput phenotyping in citrus utilizing multispectral imaging and artificial intelligence. Remote Sens., 11.","DOI":"10.3390\/rs11040410"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.compag.2018.12.003","article-title":"Estimation of nitrogen and carbon content from soybean leaf reflectance spectra using wavelet analysis under shade stress","volume":"156","author":"Chen","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10823","DOI":"10.3390\/s130810823","article-title":"A review of methods for sensing the nitrogen status in plants: Advantages, disadvantages and recent advances","volume":"13","year":"2013","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2502","DOI":"10.1080\/01431161.2012.746484","article-title":"Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: Comparison of partial least-square regression and support vector machine regression methods","volume":"34","author":"Zhai","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.isprsjprs.2016.09.002","article-title":"Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery","volume":"122","author":"Moharana","year":"2016","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_7","first-page":"101907","article-title":"Improvement of leaf nitrogen content inference in Valencia-orange trees applying spectral analysis algorithms in UAV mounted-sensor images","volume":"83","author":"Osco","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5395","DOI":"10.1109\/JSTARS.2017.2737618","article-title":"Detecting and mapping root-knot nematode infection in coffee crop using remote sensing measurements","volume":"10","author":"Martins","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","first-page":"235","article-title":"Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops","volume":"34","author":"Kross","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.agwat.2018.08.029","article-title":"Comparison of various modeling approaches for water-deficit stress monitoring in rice crop through hyperspectral remote sensing","volume":"213","author":"Krishna","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Loggenberg, K., Strever, A., Greyling, B., and Poona, N. (2018). Modeling water stress in a Shiraz vineyard using hyperspectral imaging and machine learning. Remote Sens., 10.","DOI":"10.3390\/rs10020202"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.biosystemseng.2017.11.002","article-title":"Hyperspectral machine vision as a tool for water stress severity assessment in soilless tomato crop","volume":"165","author":"Elvanidi","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_13","first-page":"1","article-title":"Water Stress in Plants: Causes, Effects, and Responses","volume":"10","author":"Lisar","year":"2012","journal-title":"Water Stress"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gerhards, M., Schlerf, M., Rascher, U., Udelhoven, T., Juszczak, R., Alberti, G., and Inoue, Y. (2018). Analysis of airborne optical and thermal imagery for detection of water stress symptoms. Remote Sens., 10.","DOI":"10.3390\/rs10071139"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Maimaitiyiming, M., Ghulam, A., Bozzolo, A., Wilkins, J.L., and Kwasniewski, M.T. (2017). Early detection of plant physiological responses to different levels of water stress using reflectance spectroscopy. Remote Sens., 9.","DOI":"10.3390\/rs9070745"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.rse.2018.06.037","article-title":"Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems","volume":"216","author":"Delloye","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.rse.2015.08.012","article-title":"Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: Scaling from leaf to image","volume":"169","author":"Kalacska","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"455","DOI":"10.13031\/2013.18308","article-title":"Determination of significant wavelengths and prediction of nitrogen content for citrus","volume":"48","author":"Min","year":"2005","journal-title":"Am. Soc. Agric. Eng."},{"key":"ref_19","first-page":"145","article-title":"Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis","volume":"11","author":"Huang","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2011.10.007","article-title":"Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera","volume":"117","author":"Berni","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111200","DOI":"10.1016\/j.rse.2019.05.019","article-title":"Spatially-explicit modelling with support of hyperspectral data can improve prediction of plant traits","volume":"231","author":"Rocha","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2016.2616418","article-title":"Advanced spectral classifiers for hyperspectral images: A review","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Index, S., Xu, N., Tian, J., Tian, Q., Xu, K., and Tang, S. (2019). Analysis of vegetation red edge with different illuminated\/shaded canopy proportions and to construct normalized difference canopy. Remote Sens., 11.","DOI":"10.3390\/rs11101192"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2018.11.015","article-title":"Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing and a multi-factor machine learning algorithm in the east of Tibetan Plateau, China","volume":"147","author":"Gao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, J., Huang, Y., Reddy, K.N., and Wang, B. (2019). Assessing crop damage from dicamba on non-dicamba-tolerant soybean by hyperspectral imaging through machine learning. Pest Manag. Sci.","DOI":"10.1002\/ps.5448"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abdulridha, J., Batuman, O., and Ampatzidis, Y. (2019). UAV-based remote sensing technique to detect citrus canker disease utilizing hyperspectral imaging and machine learning. Remote Sens., 11.","DOI":"10.3390\/rs11111373"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Karada\u011f, K., Tenekeci, M.E., and Ta\u015falt\u0131n, R.A. (2019). Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance. Sustain. Comput. Inform. Syst.","DOI":"10.1016\/j.suscom.2019.01.001"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fu, P., Meacham-Hensold, K., Guan, K., and Bernacchi, C.J. (2019). Hyperspectral leaf reflectance as proxy for photosynthetic capacities: An ensemble approach based on multiple machine learning algorithms. Front. Plant Sci.","DOI":"10.3389\/fpls.2019.00730"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7821","DOI":"10.1007\/s00253-018-9214-z","article-title":"The influence of plant growth-promoting rhizobacteria in plant tolerance to abiotic stress: A survival strategy","volume":"102","author":"Enebe","year":"2018","journal-title":"Appl. Microbiol. Biotechnol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s10661-008-0548-3","article-title":"Estimation of plant water content by spectral absorption features centered at 1,450 nm and 1,940 nm regions","volume":"157","author":"Wang","year":"2008","journal-title":"Environ. Monit. Assess."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1007\/s11274-005-3621-x","article-title":"Phytohormones and antibiotics produced by Bacillus subtilis and their effects on seed pathogenic fungi and on soybean root development","volume":"21","author":"Araujo","year":"2005","journal-title":"World J. Microbiol. Biotechnol."},{"key":"ref_32","first-page":"605","article-title":"Vescovo. Inter-comparison of hemispherical conical reflectance factors (HCRF) measured with four fiber-based spectrometers","volume":"21","author":"Anderson","year":"2013","journal-title":"Remote Sens. Sens."},{"key":"ref_33","unstructured":"FALKER (2018, November 30). ClorofiLOGElectronic: Chlorophyll Content Meter. Available online: http:\/\/www.falker.com.br\/en\/product-clorofilog-chlorophyll-meter.php."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liang, L., Di, L., Huang, T., Wang, J., Lin, L., Wang, L., and Yang, M. (2018). Estimation of leaf nitrogen content in wheat using new hyperspectral indices and a random forest regression algorithm. Remote Sens., 10.","DOI":"10.3390\/rs10121940"},{"key":"ref_35","unstructured":"Thomas, M. (1997). Mitchell. Machine Learning, McGraw-Hill, Inc.. [1st ed.]."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2797\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:37:40Z","timestamp":1760189860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2797"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,26]]},"references-count":35,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11232797"],"URL":"https:\/\/doi.org\/10.3390\/rs11232797","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,26]]}}}