{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T01:08:28Z","timestamp":1785460108777,"version":"3.56.0"},"reference-count":61,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T00:00:00Z","timestamp":1716595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","award":["001"],"award-info":[{"award-number":["001"]}]},{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico (CNPq)","award":["001"],"award-info":[{"award-number":["001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Identifying potassium (K+) deficiency in plants has traditionally been a difficult and expensive process. Traditional methods involve inspecting leaves for symptoms and conducting a laboratory analysis. These methods are not only time-consuming but also use toxic reagents. Additionally, the analysis is performed during the reproductive stage of growth, which does not allow enough time for corrective fertilization. Moreover, soybean growers do not have other tools to analyze the nutrition status during the earlier stages of development. Thus, this study proposes a quick approach for monitoring K+ in soybean crops using hyperspectral data through principal component analysis (PCA) and linear discriminant analysis (LDA) with a wavelength selection algorithm. The experiment was carried out at the Brazilian National Soybean Research Center in the 2017\u20132018, 2018\u20132019, and 2019\u20132020 soybean crop seasons, at the stages of development V4\u2013V5, R1\u2013R2, R3\u2013R4, and R5.1\u2013R5.3. Three treatments were evaluated that varied in K+ availability: severe potassium deficiency (SPD), moderate potassium deficiency (MPD), and an adequate supply of potassium (ASP). Spectral data were collected using an ASD Fieldspec 3 Jr. hyperspectral sensor. The results showed a variation in the leaf spectral signature based on the K+ availability, with SPD having higher reflectance in the visible region due to a lower concentration of pigments. PCA explained 100% of the variance across all stages and seasons, making it possible to distinguish SPD at an early development stage. LDA showed over 70% and 59% classification accuracies for discriminating a K+ deficiency in the simulation and validation stages. This study demonstrates the potential of the method as a rapid nondestructive and accurate tool for identifying K+ deficiency in soybean leaves.<\/jats:p>","DOI":"10.3390\/rs16111900","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T05:14:02Z","timestamp":1716786842000},"page":"1900","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Hyperspectral Data for Early Identification and Classification of Potassium Deficiency in Soybean Plants (Glycine max (L.) Merrill)"],"prefix":"10.3390","volume":"16","author":[{"given":"Renato Herrig","family":"Furlanetto","sequence":"first","affiliation":[{"name":"Remote Sensing and Geoprocessing Laboratory, Department of Agronomy, Maring\u00e1 State University, Maring\u00e1 87020-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu\u00eds Guilherme Teixeira","family":"Crusiol","sequence":"additional","affiliation":[{"name":"Embrapa Soja, National Soybean Research Centre, Brazilian Agricultural Research Corporation, Londrina 86085-981, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-2661","authenticated-orcid":false,"given":"Marcos Rafael","family":"Nanni","sequence":"additional","affiliation":[{"name":"Remote Sensing and Geoprocessing Laboratory, Department of Agronomy, Maring\u00e1 State University, Maring\u00e1 87020-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adilson","family":"de Oliveira Junior","sequence":"additional","affiliation":[{"name":"Embrapa Soja, National Soybean Research Centre, Brazilian Agricultural Research Corporation, Londrina 86085-981, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8711-438X","authenticated-orcid":false,"given":"Rubson Natal Ribeiro","family":"Sibaldelli","sequence":"additional","affiliation":[{"name":"Embrapa Soja, National Soybean Research Centre, Brazilian Agricultural Research Corporation, Londrina 86085-981, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3243","DOI":"10.1080\/01431161.2019.1673914","article-title":"UAV-Based Thermal Imaging in the Assessment of Water Status of Soybean Plants","volume":"41","author":"Crusiol","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Crusiol, L.G.T., Nanni, M.R., Furlanetto, R.H., Sibaldelli, R.N.R., Cezar, E., Sun, L., Foloni, J.S.S., Mertz-Henning, L.M., Nepomuceno, A.L., and Neumaier, N. (2021). Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf-Based Hyperspectral Reflectance. Remote Sens., 13.","DOI":"10.3390\/rs13020172"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1007\/s11119-022-09876-5","article-title":"Strategies for Monitoring Within-Field Soybean Yield Using Sentinel-2 Vis-NIR-SWIR Spectral Bands and Machine Learning Regression Methods","volume":"23","author":"Crusiol","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Crusiol, L.G.T., Nanni, M.R., Furlanetto, R.H., Sibaldelli, R.N.R., Cezar, E., Sun, L., Foloni, J.S.S., Mertz-Henning, L.M., Nepomuceno, A.L., and Neumaier, N. (2021). Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression. Remote Sens., 13.","DOI":"10.3390\/rs13050977"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108089","DOI":"10.1016\/j.agwat.2022.108089","article-title":"Assessing the Sensitive Spectral Bands for Soybean Water Status Monitoring and Soil Moisture Prediction Using Leaf-Based Hyperspectral Reflectance","volume":"277","author":"Crusiol","year":"2023","journal-title":"Agric. Water Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8165","DOI":"10.1080\/01431161.2021.1975841","article-title":"Using Leaf-Based Hyperspectral Reflectance for Genotype Classification within a Soybean Germplasm Collection Assessed under Different Levels of Water Availability","volume":"42","author":"Braga","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2560","DOI":"10.1080\/01431161.2022.2064198","article-title":"Assessing Phosphorus Nutritional Status in Maize Plants Using Leaf-Based Hyperspectral Measurements and Multivariate Analysis","volume":"43","author":"Furlanetto","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bandara, A.Y., Weerasooriya, D.K., Bradley, C.A., Allen, T.W., and Esker, P.D. (2020). Dissecting the Economic Impact of Soybean Diseases in the United States over Two Decades. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0231141"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1007\/s11769-011-0481-1","article-title":"Effect of Agricultural Land Use Changes on Soil Nutrient Use Efficiency in an Agricultural Area, Beijing, China","volume":"21","author":"Chen","year":"2011","journal-title":"Chin. Geogr. Sci."},{"key":"ref_10","first-page":"e0170305","article-title":"Biometric Responses of Soybean to Different Potassium Fertilization Management Practices in Years with High and Low Precipitation","volume":"42","author":"Minato","year":"2018","journal-title":"Rev. Bras. Cienc. Solo"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8783","DOI":"10.1080\/01431161.2020.1871091","article-title":"Identification and Quantification of Potassium (K+) Deficiency in Maize Plants Using an Unmanned Aerial Vehicle and Visible\/near-Infrared Semi-Professional Digital Camera","volume":"42","author":"Furlanetto","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.plaphy.2022.01.001","article-title":"Potassium in Plants: Growth Regulation, Signaling, and Environmental Stress Tolerance","volume":"172","author":"Johnson","year":"2022","journal-title":"Plant Physiol. Biochem."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, Q., Fu, H., Zhu, B., Hussain, H.A., Zhang, K., Tian, X., Duan, M., Xie, X., and Wang, L. (2021). Potassium Improves Drought Stress Tolerance in Plants by Affecting Root Morphology, Root Exudates, and Microbial Diversity. Metabolites, 11.","DOI":"10.3390\/metabo11030131"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"115278","DOI":"10.1016\/j.indcrop.2022.115278","article-title":"Estimating Technological Parameters and Stem Productivity of Sugarcane Treated with Rock Powder Using a Proximal Spectroradiometer Vis-NIR-SWIR","volume":"186","author":"Rodrigues","year":"2022","journal-title":"Ind. Crops Prod."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107746","DOI":"10.1016\/j.compag.2023.107746","article-title":"New Approach for Rapid Estimation of Leaf Nitrogen, Phosphorus, and Potassium Contents in Apple-Trees Using Vis\/NIR Spectroscopy Based on Wavelength Selection Coupled with Machine Learning","volume":"207","author":"Azadnia","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2264","DOI":"10.1007\/s11119-023-10040-w","article-title":"Machine Learning as a Tool to Predict Potassium Concentration in Soybean Leaf Using Hyperspectral Data","volume":"24","author":"Furlanetto","year":"2023","journal-title":"Precis. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Siedliska, A., Baranowski, P., Pastuszka-Wo\u017aniak, J., Zubik, M., and Krzyszczak, J. (2021). Identification of Plant Leaf Phosphorus Content at Different Growth Stages Based on Hyperspectral Reflectance. BMC Plant Biol., 21.","DOI":"10.1186\/s12870-020-02807-4"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/2150704X.2018.1547445","article-title":"Remote Estimation of Fraction of Radiation Absorbed by Photosynthetically Active Vegetation: Generic Algorithm for Maize and Soybean","volume":"10","author":"Gitelson","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.envexpbot.2018.02.006","article-title":"When Are Foliar Anthocyanins Useful to Plants? Re-Evaluation of the Photoprotection Hypothesis Using Arabidopsis thaliana Mutants That Differ in Anthocyanin Accumulation","volume":"154","author":"Gould","year":"2018","journal-title":"Environ. Exp. Bot."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Falcioni, R., Gon\u00e7alves, J.V.F., de Oliveira, K.M., de Oliveira, C.A., Reis, A.S., Crusiol, L.G.T., Furlanetto, R.H., Antunes, W.C., Cezar, E., and de Oliveira, R.B. (2023). Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy. Plants, 12.","DOI":"10.3390\/plants12193424"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s40626-023-00268-2","article-title":"Nutrient Deficiency Lowers Photochemical and Carboxylation Efficiency in Tobacco","volume":"35","author":"Falcioni","year":"2023","journal-title":"Theor. Exp. Plant Physiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"153161","DOI":"10.1016\/j.jplph.2020.153161","article-title":"High Resolution Leaf Spectral Signature as a Tool for Foliar Pigment Estimation Displaying Potential for Species Differentiation","volume":"249","author":"Falcioni","year":"2020","journal-title":"J. Plant Physiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110316","DOI":"10.1016\/j.plantsci.2019.110316","article-title":"Investigating Potato Late Blight Physiological Differences across Potato Cultivars with Spectroscopy and Machine Learning","volume":"295","author":"Gold","year":"2020","journal-title":"Plant Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gold, K.M., Townsend, P.A., Chlus, A., Herrmann, I., Couture, J.J., Larson, E.R., and Gevens, A.J. (2020). Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato. Remote Sens., 12.","DOI":"10.3390\/rs12020286"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1093\/treephys\/tpaa005","article-title":"Spectral Differentiation of Oak Wilt from Foliar Fungal Disease and Drought Is Correlated with Physiological Changes","volume":"40","author":"Fallon","year":"2020","journal-title":"Tree Physiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1007\/s40858-020-00387-0","article-title":"Leaf Hyperspectral Reflectance as a Potential Tool to Detect Diseases Associated with Vineyard Decline","volume":"45","author":"Junges","year":"2020","journal-title":"Trop. Plant Pathol."},{"key":"ref_27","first-page":"100400","article-title":"Hyperspectral Reflectance Imaging to Classify Lettuce Varieties by Optimum Selected Wavelengths and Linear Discriminant Analysis","volume":"20","author":"Furlanetto","year":"2020","journal-title":"Remote Sens. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"611","DOI":"10.21273\/JASHS.132.5.611","article-title":"Assessing Nitrogen and Potassium Deficiencies in Olive Orchards through Discriminant Analysis of Hyperspectral Data","volume":"132","year":"2007","journal-title":"J. Am. Soc. Hortic. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2252","DOI":"10.1080\/01431161.2016.1171926","article-title":"Discriminant Analysis of Nitrogen Treatments in Switchgrass and High Biomass Sorghum Using Leaf and Canopy-Scale Reflectance Spectroscopy","volume":"37","author":"Foster","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","unstructured":"EMBRAPA (2013). Tecnologias de Produ\u00e7\u00e3o de Soja\u2014Regi\u00e3o Central Do Brasil\u20142014, Embrapa Soja."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.biosystemseng.2005.02.007","article-title":"Hyperspectral Crop Reflectance Data for Characterising and Estimating Fungal Disease Severity in Wheat","volume":"91","author":"Muhammed","year":"2005","journal-title":"Biosyst. Eng."},{"key":"ref_32","unstructured":"Muhammed, H.H. (2002, January 16\u201318). Using Hyperspectral Reflectance Data for Discrimination between Healthy and Diseased Plants, and Determination of Damage-Level in Diseased Plants. Proceedings of the 2002 Applied Imagery Pattern Recognition Workshop, Washington, DC, USA."},{"key":"ref_33","unstructured":"Farias, J.R.B., Nepomuceno, A.L., and Neumaier, N. (2007). Ecofisiologia Da Soja, Embrapa Soja."},{"key":"ref_34","unstructured":"Oliveira Junior, A.D., Castro, C.D., Pereira, L.R., and Domingos, C.D.S. (2016). Est\u00e1dios Fenol\u00f3gicos e Marcha de Absor\u00e7\u00e3o de Nutrientes Da Soja, Embrapa."},{"key":"ref_35","unstructured":"EMBRAPA (2009). Manual de An\u00e1lises Qu\u00edmicas de Solos, Plantas e Fertilizantes, Embrapa Solos."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.aca.2005.03.075","article-title":"Outliers in Partial Least Squares Regression","volume":"544","author":"Ortiz","year":"2005","journal-title":"Anal. Chim. Acta"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Varmuza, K., and Filzmoser, P. (2016). Introduction to Multivariate Statistical Analysis in Chemometrics, CRC Press.","DOI":"10.1201\/9781420059496"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"169","DOI":"10.3233\/AIC-170729","article-title":"Linear Discriminant Analysis: A Detailed Tutorial","volume":"30","author":"Tharwat","year":"2017","journal-title":"AI Commun."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Izenman, A.J. (2013). Linear Discriminant Analysis, Springer.","DOI":"10.1007\/978-0-387-78189-1_8"},{"key":"ref_40","unstructured":"Sociedade Brasileira de Ciencia do Solo (2017). Manual de Aduba\u00e7\u00e3o e Calagem Para o Estado Do Paran\u00e1 Curitiba, Sociedade Brasileira de Ci\u00eancia do Solo\u2014N\u00facleo Estadual Paran\u00e1."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1046\/j.1365-3040.1997.d01-89.x","article-title":"Effect of Water Restriction on Carbohydrate Metabolism and Photosynthesis in Mature Maize Leaves","volume":"20","author":"Pelleschi","year":"1997","journal-title":"Plant Cell Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1104\/pp.124.1.71","article-title":"A Maize Vacuolar Invertase, IVR 2, Is Induced by Water Stress. Organ\/Tissue Specificity and Diurnal Modulation of Expression","volume":"124","author":"Kim","year":"2000","journal-title":"Plant Physiol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1017\/S0021859615000313","article-title":"The Soybean Yield Gap in Brazil\u2014Magnitude, Causes and Possible Solutions for Sustainable Production","volume":"153","author":"Sentelhas","year":"2015","journal-title":"J. Agric. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112863","DOI":"10.1016\/j.scienta.2024.112863","article-title":"Blue Light Strongly Promotes De-Etiolation over Green, Moderate over Red, but Have Limited Action over Far-Red Lights in Lettuce Plants","volume":"328","author":"Pattaro","year":"2024","journal-title":"Sci. Hortic."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.plantsci.2018.10.012","article-title":"Nitrogen-Improved Photosynthesis Quantum Yield Is Driven by Increased Thylakoid Density, Enhancing Green Light Absorption","volume":"278","author":"Moriwaki","year":"2019","journal-title":"Plant Sci."},{"key":"ref_46","first-page":"44","article-title":"Effects of Different Potassium Stress on Leaf Photosynthesis and Chlorophyll Fluorescence in Maize (Zea mays L.) at Seedling Stage","volume":"7","author":"Zhao","year":"2016","journal-title":"Agric. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1023\/A:1012404204910","article-title":"Influence of Potassium Deficiency on Photosynthesis, Chlorophyll Content, and Chloroplast Ultrastructure of Cotton Plants","volume":"39","author":"Zhao","year":"2001","journal-title":"Photosynthetica"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.asr.2004.09.008","article-title":"Changes in Spectral Reflectance of Wheat Leaves in Response to Specific Macronutrient Deficiency","volume":"35","author":"Beyl","year":"2005","journal-title":"Adv. Space Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1007\/s11119-019-09670-w","article-title":"Monitoring Leaf Potassium Content Using Hyperspectral Vegetation Indices in Rice Leaves","volume":"21","author":"Lu","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e20180409","DOI":"10.1590\/1678-992x-2018-0409","article-title":"Estimation of Leaf Nutrient Concentration from Hyperspectral Reflectance in Eucalyptus Using Partial Least Squares Regression","volume":"77","author":"Santana","year":"2020","journal-title":"Sci. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kumar, L., Schmidt, K., Dury, S., and Skidmore, A. (2002). Imaging Spectrometry and Vegetation Science, Springer.","DOI":"10.1007\/978-0-306-47578-8_5"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.rse.2003.11.001","article-title":"Predicting in Situ Pasture Quality in the Kruger National Park, South Africa, Using Continuum-Removed Absorption Features","volume":"89","author":"Mutanga","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1111\/j.1744-7909.2007.00358.x","article-title":"Oxidative Stress and Antioxidant Responses in Young Leaves of Mulberry Plants Grown Under Nitrogen, Phosphorus or Potassium Deficiency","volume":"49","author":"Tewari","year":"2007","journal-title":"J. Integr. Plant Biol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"7370","DOI":"10.3390\/ijms14047370","article-title":"The Critical Role of Potassium in Plant Stress Response","volume":"14","author":"Wang","year":"2013","journal-title":"Int. J. Mol. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1104\/pp.111.4.1077","article-title":"Regulation of Arabidopsis thaliana (L.) Heynh Arginine Decarboxylase by Potassium Deficiency Stress","volume":"111","author":"Watson","year":"1996","journal-title":"Plant Physiol."},{"key":"ref_56","unstructured":"Globe, D. (2009). The Benefits of the 8 Spectral Bands of WorldView-2, Digital Globe. Available online: https:\/\/www.yumpu.com\/en\/document\/read\/10428472\/the-benefits-of-the-8-spectral-bands-of-worldview-2-digitalglobe."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"4177","DOI":"10.1080\/01431161.2021.1890855","article-title":"Identification and Classification of Asian Soybean Rust Using Leaf-Based Hyperspectral Reflectance","volume":"42","author":"Furlanetto","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5290","DOI":"10.1109\/ACCESS.2017.2763596","article-title":"Variable Selection and Optimization in Rapid Detection of Soybean Straw Biomass Based on CARS","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/BF02370385","article-title":"Water Stress Effects on Leaf Elongation, Leaf Water Potential, Transpiration, and Nutrient Uptake of Rice, Maize, and Soybean","volume":"103","author":"Tanguilig","year":"1987","journal-title":"Plant Soil."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1081\/PLN-120030673","article-title":"Size Distribution and Mineral Nutrients of Soybean Seeds in Response to Drought Stress","volume":"27","author":"Samarah","year":"2004","journal-title":"J. Plant Nutr."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1900\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:48:34Z","timestamp":1760107714000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1900"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,25]]},"references-count":61,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16111900"],"URL":"https:\/\/doi.org\/10.3390\/rs16111900","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,25]]}}}