{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:50:26Z","timestamp":1792806626346,"version":"4.5.0"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T00:00:00Z","timestamp":1590710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Global Institute for Food Security (GIFS), University of Saskatchewan, Canada","award":["418406"],"award-info":[{"award-number":["418406"]}],"id":[{"id":"https:\/\/ror.org\/010x8gc63","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop water stress is a deficiency in plants in water supply when the transpiration rate becomes higher than the water absorption capacity. The stress may be detected by a reduction in soil water content, or by the change in physiological properties of the crop. The leaf water content (LWC) is commonly used to assess the water status of plants, which is one of the indicators of crop water stress. In this work, the leaf relative water contents of four different crops: canola, wheat, soybeans, and corn\u2014all in vegetative growth stage\u2014were determined by a noninvasive tool called, electrical impedance spectroscopy (EIS). Using a frequency range of 5\u201315 kHz, a strong correlation between leaf water contents and leaf impedances was obtained using multiple linear regression. The trained dataset was validated by analysis of variance tests. Regression results were obtained using the least square method. The optimized regression model coefficients for different crops were proposed by selecting features using the wrapper backward elimination method. Multi-collinearity among the features was considered and individual T-tests were made in the feature selection. A maximum correlation coefficient (R) of 0.99 was obtained for canola compared to the other crops; the corresponding coefficient of determination (R2) of 0.98, an adjusted R2 of 0.93, and root mean square error (rmse) of 0.30% were obtained for 36 features. Therefore, the results show that the proposed technique using EIS can be used to develop a low-cost and effective tool for determining the leaf water contents rapidly and efficiently in multiple crops.<\/jats:p>","DOI":"10.3390\/rs12111753","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T09:19:27Z","timestamp":1591089567000},"page":"1753","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Rapid and Efficient Determination of Relative Water Contents of Crop Leaves Using Electrical Impedance Spectroscopy in Vegetative Growth Stage"],"prefix":"10.3390","volume":"12","author":[{"given":"Rinku","family":"Basak","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0125-9789","authenticated-orcid":false,"given":"Khan A.","family":"Wahid","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anh","family":"Dinh","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9345-9640","authenticated-orcid":false,"given":"Raju","family":"Soolanayakanahally","sequence":"additional","affiliation":[{"name":"Agriculture and Agri-Food Canada, Saskatoon Research and Development Centre, Saskatoon, SK S7N 0X2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Fotouhi","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aryan S.","family":"Mehr","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.compag.2017.07.026","article-title":"Recent advances in crop water stress detection","volume":"141","author":"Ihuoma","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1104\/pp.113.219006","article-title":"Improving photosynthesis","volume":"162","author":"Evans","year":"2013","journal-title":"Plant Physiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.2134\/agronj2000.9261221x","article-title":"Determination of crop water stress index for irrigation timing and yield estimation of corn","volume":"92","author":"Irmak","year":"2000","journal-title":"Agron. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1007\/s11119-011-9252-3","article-title":"A plant based sensing method for nutrition stress monitoring","volume":"13","author":"Tomkiewicz","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"22203","DOI":"10.1117\/1.JRS.12.022203","article-title":"Estimation of leaf water content from mid- and thermal-infrared spectra by coupling genetic algorithm and partial least squares regression","volume":"12","author":"Arshad","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fpls.2017.00721","article-title":"Determination of leaf water content by visible and near-infrared spectrometry and multivariate calibration in miscanthus","volume":"8","author":"Jin","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1155\/2012\/276795","article-title":"Rapid determination of leaf water content using VIS\/NIR spectroscopy analysis with wavelength selection","volume":"27","author":"Zhang","year":"2012","journal-title":"Spectrosc. Int. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kovar, M., Brestic, M., Sytar, O., Barek, V., Hauptvogel, P., and Zivcak, M. (2019). Evaluation of hyperspectral reflectance parameters to assess the leaf water content in soybean. Water, 11.","DOI":"10.3390\/w11030443"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-017-0197-z","article-title":"Non-invasive absolute measurement of leaf water content using terahertz quantum cascade lasers","volume":"13","author":"Baldacci","year":"2017","journal-title":"Plant Methods"},{"key":"ref_10","first-page":"178","article-title":"Measurements and analysis of water content in winter wheat leaf based on terahertz spectroscopy","volume":"11","author":"Li","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_11","first-page":"359","article-title":"Electrical impedance measurement on plants: A review with some insights to other fields","volume":"31","year":"2019","journal-title":"Exp. Plant Physiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11492","DOI":"10.3390\/s140711492","article-title":"An analysis of electrical impedance measurements applied for plant N status estimation in lettuce (Lactuca sativa)","volume":"14","year":"2014","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-016-0133-7","article-title":"Benchmarking electrical methods for rapid estimation of root biomass","volume":"12","author":"Postic","year":"2016","journal-title":"Plant Methods"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1515\/intag-2015-0013","article-title":"Relationship between moisture content and electrical impedance of carrot slices during drying","volume":"29","year":"2015","journal-title":"Int. Agrophys."},{"key":"ref_15","first-page":"58","article-title":"The prospect of electrical impedance spectroscopy as non-destructive evaluation of citrus fruits acidity","volume":"2","author":"Juansah","year":"2012","journal-title":"IJETAE"},{"key":"ref_16","first-page":"194","article-title":"Early diagnosis and monitoring of nitrogen nutrition stress in tomato leaves using electrical impedance spectroscopy","volume":"10","author":"Meiqing","year":"2017","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_17","first-page":"20","article-title":"Plant tissue differentiation using electrical impedance spectroscopy with deep neural networks","volume":"6","author":"Cavalieri","year":"2020","journal-title":"Int. J. Biosen. Bioelectron."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"040005","DOI":"10.1063\/1.4917112","article-title":"Low cost soil sensor based on impedance spectroscopy for in-situ measurement","volume":"1656","author":"Umar","year":"2015","journal-title":"Aip. Conf. Proc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s11104-005-7531-3","article-title":"Analysis of root growth by impedance spectroscopy (EIS)","volume":"277","author":"Bajazet","year":"2005","journal-title":"Plant Soil"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1139\/b82-084","article-title":"Studies of phosphorus and potassium deficiencies in Trifolium subterraneum based on electrical measurements","volume":"60","author":"Greenham","year":"1982","journal-title":"Can. J. Bot."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/9761356","article-title":"Electrical impedance spectroscopy for quality assessment of meat and fish: A review on basic principles, measurement methods, and recent advances","volume":"2017","author":"Zhao","year":"2017","journal-title":"J. Food Quality"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"303","DOI":"10.5194\/jsss-6-303-2017","article-title":"Electrical impedance spectroscopy (EIS) for biological analysis and food characterization: A review","volume":"6","author":"Grossi","year":"2017","journal-title":"J. Sens. Sens. Syst."},{"key":"ref_23","first-page":"134","article-title":"Measurement of fresh tea leaf growth using electrical impedance spectroscopy","volume":"2","author":"Mizukami","year":"2007","journal-title":"Agric. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1088\/0967-3334\/34\/4\/391","article-title":"A four-electrode low frequency impedance spectroscopy measurement system using the AD5933 measurement chip","volume":"34","author":"Margo","year":"2013","journal-title":"Physiol. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.compag.2016.03.006","article-title":"Electrical impedance spectroscopy: A tool to investigate the responses of one halophyte to different growth and stress conditions","volume":"123","author":"Hamed","year":"2016","journal-title":"Comput. Electron. Agricult."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Basak, R., Wahid, K., and Dinh, A. (2020). Determination of leaf nitrogen concentrations using electrical impedance spectroscopy in multiple crops. Remote Sens., 12.","DOI":"10.3390\/rs12030566"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sun, J., Yang, J., Shi, S., Chen, B., Du, L., Gong, W., and Song, S. (2017). Estimating rice leaf nitrogen concentration: Influence of regression algorithms based on passive and active leaf reflectance. Remote Sens., 9.","DOI":"10.3390\/rs9090951"},{"key":"ref_28","first-page":"88","article-title":"Correlation and regression analysis in barley","volume":"1","year":"2013","journal-title":"World Essays J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"895","DOI":"10.21475\/ajcs.2016.10.06.p7634","article-title":"Prediction of biological and grain yield of barley using multiple regression and artificial neural network models","volume":"10","author":"Mokarram","year":"2016","journal-title":"Aust. J. Crop. Sci."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s11120-014-9970-2","article-title":"Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components","volume":"120","author":"Riccardi","year":"2014","journal-title":"Photosynth. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1080\/00103624.2015.1104342","article-title":"Using artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of Saint John\u2019s wort leaves","volume":"47","author":"Odabas","year":"2016","journal-title":"Commun. Soil Sci. Plant. Anal."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, L., Song, B., Zhang, S., and Liu, X. (2017). A novel principal component analysis method for the reconstruction of leaf reflectance spectra and retrieval of leaf biochemical contents. Remote Sens., 9.","DOI":"10.3390\/rs9111113"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1753\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:33:45Z","timestamp":1760175225000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1753"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,29]]},"references-count":33,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12111753"],"URL":"https:\/\/doi.org\/10.3390\/rs12111753","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,29]]}}}