{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T20:28:34Z","timestamp":1777580914851,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images to identify healthy and individual nutrient deficiencies of grapevine leaves. Features such as mean reflectance, mean first derivative reflectance, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD) were employed at various stages in the ultraviolet (UV), visible (VIS) and near-infrared (N.I.R.) regions for our experiment. Leaves were examined visually in the laboratory and grouped as either healthy (i.e. control) or unhealthy. Then, the features of the leaves were extracted from these two groups. In a second experiment, features of individual nutrient-deficient leaves (e.g., N, K and Mg) were also analysed and compared with those of control leaves. Furthermore, a customised support vector machine (SVM) was used to demonstrate that these features can be utilised with a high degree of effectiveness to identify unhealthy samples and not only to distinguish from control and nutrient deficient but also to identify individual nutrient defects. Therefore, the proposed work corroborated that HS imaging has excellent potential to analyse features based on healthiness and individual nutrient deficiencies of grapevine leaves.<\/jats:p>","DOI":"10.3390\/rs13163317","type":"journal-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T04:22:20Z","timestamp":1629692540000},"page":"3317","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2264-8495","authenticated-orcid":false,"given":"Sourabhi","family":"Debnath","sequence":"first","affiliation":[{"name":"Computer Vision Laboratory, School of Computing and Mathematics, Charles Sturt University, Bathurst 2795, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6870-5056","authenticated-orcid":false,"given":"Manoranjan","family":"Paul","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, School of Computing and Mathematics, Charles Sturt University, Bathurst 2795, Australia"},{"name":"National Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D. M. Motiur","family":"Rahaman","sequence":"additional","affiliation":[{"name":"National Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3530-5926","authenticated-orcid":false,"given":"Tanmoy","family":"Debnath","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, School of Computing and Mathematics, Charles Sturt University, Bathurst 2795, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5728-4356","authenticated-orcid":false,"given":"Lihong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, School of Computing and Mathematics, Charles Sturt University, Bathurst 2795, Australia"},{"name":"National Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8417-0995","authenticated-orcid":false,"given":"Tintu","family":"Baby","sequence":"additional","affiliation":[{"name":"National Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9765-5510","authenticated-orcid":false,"given":"Leigh M.","family":"Schmidtke","sequence":"additional","affiliation":[{"name":"National Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6637-3561","authenticated-orcid":false,"given":"Suzy Y.","family":"Rogiers","sequence":"additional","affiliation":[{"name":"National Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, Australia"},{"name":"NSW Department of Primary Industries, Regional NSW, Wollongbar 2478, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kishore, M., and Kulkarni, S.B. (2015, January 17\u201319). Hyperspectral imaging technique for plant leaf identification. Proceedings of the 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology ICERECT 2015, Mandya, India.","DOI":"10.1109\/ERECT.2015.7499014"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.compag.2010.07.008","article-title":"Hyperspectral waveband selection for internal defect detection of pickling cucumbers and whole pickles. Comput","volume":"74","author":"Ariana","year":"2010","journal-title":"Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.jfoodeng.2011.05.002","article-title":"Studies on banana fruit quality and maturity stages using hyperspectral imaging","volume":"108","author":"Rajkumar","year":"2012","journal-title":"J. Food Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.jfoodeng.2016.01.002","article-title":"Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine","volume":"179","author":"Zhang","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.foodchem.2016.09.023","article-title":"How to predict the sugariness and hardness of melons: A near-infrared hyperspectral imaging method","volume":"218","author":"Sun","year":"2017","journal-title":"Food Chem."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.snb.2018.06.121","article-title":"Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging","volume":"273","author":"Strajnar","year":"2018","journal-title":"Sens. Actuators B Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.compag.2016.07.028","article-title":"Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging. Comput","volume":"127","author":"Ge","year":"2016","journal-title":"Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.3389\/fpls.2017.01348","article-title":"High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging","volume":"8","author":"Pandey","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_9","first-page":"5","article-title":"Evaluation of Water Potentials of Leafy Vegetables Using Hyperspectral Imaging","volume":"51","author":"Tung","year":"2018","journal-title":"IFAC-Pap."},{"key":"ref_10","unstructured":"Moghadam, P., Ward, D., Goan, E., Jayawardena, S., Sikka, P., and Hernandez, E. (December, January 29). Plant disease detection using hyperspectral imaging. Proceedings of the International Conference on Digital Image Computing: Techniques and Applications, Sydney, NSW, Australia."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s13007-017-0233-z","article-title":"Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress","volume":"13","author":"Lowe","year":"2017","journal-title":"Plant Methods"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jfoodeng.2013.12.032","article-title":"Hyperspectral near-infrared imaging for the detection of physical damages of pear","volume":"130","author":"Lee","year":"2014","journal-title":"J. Food Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1016\/j.foodchem.2017.06.007","article-title":"Determination of total iron-reactive phenolics, anthocyanins and tannins in wine grapes of skins and seeds based on near-infrared hyperspectral imaging","volume":"237","author":"Zhang","year":"2017","journal-title":"Food Chem."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.foodchem.2017.10.027","article-title":"Evaluation of extractable polyphenols released to wine from cooperage byproduct by near infrared hyperspectral imaging","volume":"244","author":"Heredia","year":"2018","journal-title":"Food Chem."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.compag.2017.06.009","article-title":"Comparison of different approaches for the prediction of sugar content in new vintages of whole Port wine grape berries using hyperspectral imaging","volume":"140","author":"Gomes","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.3389\/fpls.2018.01102","article-title":"On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties","volume":"9","author":"Novales","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Loggenberg, K., Strever, A., Greyling, B., and Poona, N. (2018). Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning. Remote. Sens., 10.","DOI":"10.3390\/rs10020202"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bendel, N., Kicherer, A., Backhaus, A., K\u00f6ckerling, J., Maixner, M., Bleser, E., Kl\u00fcck, H.-C., Seiffert, U., Voegele, R.T., and T\u00f6pfer, R. (2020). Detection of Grapevine Leafroll-Associated Virus 1 and 3 in White and Red Grapevine Cultivars Using Hyperspectral Imaging. Remote. Sens., 12.","DOI":"10.3390\/rs12101693"},{"key":"ref_19","first-page":"98400","article-title":"Detecting red blotch disease in grape leaves using hyperspectral imaging","volume":"9840","author":"Mehrubeoglu","year":"2016","journal-title":"Algorithms Technol. Multispectral Hyperspectral Ultraspectral Imag. XXII"},{"key":"ref_20","unstructured":"Retallack, M. (2021, March 09). GRAPEVINE BIOLOGY. Available online: http:\/\/www.viti.com.au\/pdf\/MVWGG%20Fact%20Sheet\u2014Grapevine%20Biology.pdf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/hortres.2014.2","article-title":"A rapid dehydration leaf assay reveals stomatal response differences in grapevine genotypes","volume":"1","author":"Hopper","year":"2014","journal-title":"Hortic. Res."},{"key":"ref_22","unstructured":"Fisher, D., and Wicks, T. (2021, March 09). Powdery Mildew in Wine Grapes in Western Australia, Available online: https:\/\/researchlibrary.agric.wa.gov.au\/bulletins."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-017-0198-y","article-title":"Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images","volume":"13","author":"Knauer","year":"2017","journal-title":"Plant Methods"},{"key":"ref_24","unstructured":"Proffitt, T., and Campbell-Clause, J. (2021, March 09). Managing Grapevine Nutrition and Vineyard Soil Health Perth Region NRM. Available online: www.winewa.asn.au."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.scienta.2016.09.036","article-title":"Effects of nitrogen and irrigation on the quality of grapes and the susceptibility to Botrytis bunch rot","volume":"212","author":"Thomidis","year":"2016","journal-title":"Sci. Hortic."},{"key":"ref_26","first-page":"451","article-title":"Using foliar applications of magnesium and potassium to improve yields and some qualitative parameters of vine grapes (Vitis vinifera L.)","volume":"61","author":"Elbl","year":"2016","journal-title":"Plant Soil Environ."},{"key":"ref_27","first-page":"7","article-title":"Using hyperspectral remote sensing to map grape quality in \u2018Tempranillo\u2019 vineyards affected by iron deficiency chlorosis","volume":"46","year":"2007","journal-title":"J. Grapevine Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Debnath, T., Debnath, S., and Paul, M. (2019). Detection of Age and Defect of Grapevine Leaves Using Hyper Spectral Imaging. Transactions on Petri Nets and Other Models of Concurrency XV, Springer Science and Business Media.","DOI":"10.1007\/978-3-030-34879-3_8"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"30370","DOI":"10.1109\/ACCESS.2018.2844405","article-title":"Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_30","first-page":"223","article-title":"Model-based statistical features for mobile phone image of tomato plant disease classification","volume":"2017","author":"Hlaing","year":"2018","journal-title":"Parallel Distrib. Comput. Appl. Technol. PDCAT Proc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bin Husin, Z., Shakaff, A.Y.B.M., Aziz, A.H.B.A., and Farook, R.B.S.M. (2012, January 8\u201310). Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques. Proceedings of the Third International Conference on Intelligent Systems Modelling and Simulation, Kota, Kinabalu.","DOI":"10.1109\/ISMS.2012.33"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Islam, M., Anh, D., Wahid, K., and Bhowmik, P. (May, January 30). Detection of potato diseases using image segmentation and multiclass support vector machine. Proceedings of the 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada.","DOI":"10.1109\/CCECE.2017.7946594"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1109\/JSTARS.2013.2294961","article-title":"New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Rahaman, D.M.M., Baby, T., Oczkowski, A., Paul, M., Zheng, L., Schmidtke, L., Holzapfel, B.P., Walker, R.R., and Rogiers, S.Y. (2019). Grapevine Nutritional Disorder Detection Using Image Processing. Transactions on Petri Nets and Other Models of Concurrency XV, Springer Science and Business Media.","DOI":"10.1007\/978-3-030-34879-3_15"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Phadikar, S., and Sil, J. (2008, January 24\u201327). Rice disease identification using pattern recognition techniques. Proceedings of the 11th International Conference on Computer and Information Technology, Khulna, Bangladesh.","DOI":"10.1109\/ICCITECHN.2008.4803079"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ren, T., Zhang, Y., and Wang, C. (2019, January 28\u201330). Identification of Corn Leaf Disease Based on Image Processing. Proceedings of the 2nd International Conference on Information Systems and Computer Aided Education ICISCAE 2019, Dalian, China.","DOI":"10.1109\/ICISCAE48440.2019.221610"},{"key":"ref_37","unstructured":"Li, C., and Lanying, W. (2011). Research on Application of Probability Neural Network in Maize Leaf Disease Identification. J. Agric. Mech. Researc, 6, Available online: http:\/\/en.cnki.com.cn\/Article_en\/CJFDTOTAL-NJYJ201106040.htm."},{"key":"ref_38","first-page":"194","article-title":"Corn leaf disease identification based on multiple classifiers fusion","volume":"31","author":"Liangfeng","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_39","first-page":"1","article-title":"Maize Leaf Diseases Recognition and Classification Based on Imaging and Machine Learning Techniques","volume":"5","author":"Alehegn","year":"2017","journal-title":"Int. J. Innov. Res. Comput. Commun. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"261","DOI":"10.5344\/ajev.2014.13121","article-title":"Modified Method for Producing Grapevine Plants in Controlled Environments","volume":"65","author":"Baby","year":"2014","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Neuwirthov\u00e1, E., Lhot\u00e1kov\u00e1, Z., and Albrechtova, J. (2017). The Effect of Leaf Stacking on Leaf Reflectance and Vegetation Indices Measured by Contact Probe during the Season. Sensors, 17.","DOI":"10.3390\/s17061202"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/s13337-013-0161-0","article-title":"Spectral reflectance pattern in soybean for assessing yellow mosaic disease","volume":"24","author":"Gazala","year":"2013","journal-title":"Indian J. Virol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4020","DOI":"10.1016\/j.rse.2008.05.019","article-title":"Evaluating hyperspectral imaging of wetland vegetation as a tool for detecting estuarine nutrient enrichment","volume":"112","author":"Siciliano","year":"2008","journal-title":"Remote. Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.isprsjprs.2007.02.001","article-title":"Red edge shift and biochemical content in grass canopies","volume":"62","author":"Mutanga","year":"2007","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.scienta.2018.06.097","article-title":"Iron, magnesium, nitrogen and potassium deficiency symptom discrimination by reflectance spectroscopy in grapevine leaves","volume":"241","author":"Rustioni","year":"2018","journal-title":"Sci. Hortic."},{"key":"ref_46","unstructured":"(2021, August 07). NDVI and Your Farm: Understanding NDVI for Plant Health. Available online: https:\/\/www.agriculture-xprt.com\/news\/ndvi-and-your-farm-understanding-ndvi-for-plant-health-insights-702065."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Vabalas, A., Gowen, E., Poliakoff, E., and Casson, A.J. (2019). Machine learning algorithm validation with a limited sample size. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224365"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Seng, K., Ang, L., Liew, A.C., and Gao, J. (2019). Multimodal Information Processing and Big Data Analytics in a Digital World. Multimodal Analytics for Next-Generation Big Data Technologies and Applications, Springer Nature Switzerland AG.","DOI":"10.1007\/978-3-319-97598-6"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TCSVT.2018.2844780","article-title":"Spatial and Motion Saliency Prediction Method Using Eye Tracker Data for Video Summarization","volume":"29","author":"Paul","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3317\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:49:27Z","timestamp":1760165367000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,23]]},"references-count":49,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163317"],"URL":"https:\/\/doi.org\/10.3390\/rs13163317","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,23]]}}}