{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T08:30:00Z","timestamp":1780389000311,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,21]],"date-time":"2020-07-21T00:00:00Z","timestamp":1595289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701242"],"award-info":[{"award-number":["61701242"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["KJQN201844"],"award-info":[{"award-number":["KJQN201844"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["KYGX201703"],"award-info":[{"award-number":["KYGX201703"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases.<\/jats:p>","DOI":"10.3390\/s20144045","type":"journal-article","created":{"date-parts":[[2020,7,21]],"date-time":"2020-07-21T06:38:55Z","timestamp":1595313535000},"page":"4045","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model"],"prefix":"10.3390","volume":"20","author":[{"given":"Yuhua","family":"Li","sequence":"first","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihui","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengjie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingxu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pawar, P., Turkar, V., and Patil, P. (2016, January 26\u201327). Cucumber disease detection using artificial neural network [C]. Proceedings of the International Conference on Inventive Computation Technologies, Coimbatore, India.","DOI":"10.1109\/INVENTIVE.2016.7830151"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","article-title":"Plant disease detection by imaging sensors\u2013parallels and specific demands for precision agriculture and plant phenotyping","volume":"100","author":"Mahlein","year":"2016","journal-title":"Plant Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s11831-018-9255-6","article-title":"Plants disease identification and classification through leaf images: A survey","volume":"26","author":"Kaur","year":"2019","journal-title":"Arch. Comput. Method Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, F., Sun, Y., and Wang, Y. (2020). Graph constraint and collaborative representation classifier steered discriminative projection with applications for the early identification of cucumber diseases. Sensors, 20.","DOI":"10.3390\/s20041217"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2010.02.007","article-title":"Review: A review of advanced techniques for detecting plant diseases","volume":"72","author":"Sankaran","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105220","DOI":"10.1016\/j.compag.2020.105220","article-title":"New perspectives on plant disease characterization based on deep learning","volume":"170","author":"Lee","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","unstructured":"El-Helly, M., Rafea, A.A., and El-Gammal, S. (2003, January 18\u201320). An integrated image processing system for leaf disease detection and diagnosis. Proceedings of the Indian International Conference on Artificial Intelligence, Hyderabad, India."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.compag.2017.01.014","article-title":"Leaf image based cucumber disease recognition using sparse representation classification","volume":"134","author":"Zhang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3289801","DOI":"10.1155\/2016\/3289801","article-title":"Deep neural networks based recognition of plant diseases by leaf image classification","volume":"2016","author":"Sladojevic","year":"2016","journal-title":"Comput. Intel. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","article-title":"Deep learning models for plant disease detection and diagnosis","volume":"145","author":"Ferentinos","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","first-page":"115","article-title":"Recognition for cucumber disease based on leaf spot shape and neural network","volume":"29","author":"Jia","year":"2013","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_13","first-page":"62","article-title":"Sunflower leaf diseases detection using image segmentation based on particle swarm optimization","volume":"3","author":"Singh","year":"2019","journal-title":"Artif. Intel. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.neucom.2016.04.034","article-title":"Cucumber disease recognition based on global-local singular value decomposition","volume":"205","author":"Zhang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"22482","DOI":"10.1038\/srep22482","article-title":"Plant phenotyping using probabilistic topic models: Uncovering the hyperspectral language of plants","volume":"6","author":"Wahabzada","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lopezlopez, M., Calderon, R., Gonzalezdugo, V., Zarotejada, P.J., and Fereres, E. (2016). Early detection and quantification of almond red leaf blotch using high-resolution hyperspectral and thermal imagery. Remote. Sens., 8.","DOI":"10.3390\/rs8040276"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.compag.2010.06.009","article-title":"Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance","volume":"74","author":"Rumpf","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105039","DOI":"10.1016\/j.compag.2019.105039","article-title":"Detection of anthracnose in tea plants based on hyperspectral imaging","volume":"167","author":"Yuan","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","first-page":"31","article-title":"Real-Time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning","volume":"4","author":"Gao","year":"2020","journal-title":"Artif. Intel. Agric."},{"key":"ref_20","first-page":"202","article-title":"Diagnosis method of cucumber disease with hyperspectral imaging in greenhouse","volume":"26","author":"Tian","year":"2010","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_21","unstructured":"Xue, H. (2016). Machine Learning, Tsinghua University Press. [1st ed.]."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4344","DOI":"10.1109\/JSTARS.2016.2575360","article-title":"An investigation into machine learning regression techniques for the leaf rust disease detection using hyperspectral measurement","volume":"9","author":"Ashourloo","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_23","unstructured":"Zhang, L., Yang, M., and Feng, X. (2011, January 6). Sparse representation or collaborative representation: Which helps face recognition?. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain."},{"key":"ref_24","first-page":"174","article-title":"Critical review of fast detection of crop nutrient and physiological information with spectral and imaging technology","volume":"31","author":"He","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_25","first-page":"2745","article-title":"Research on spectra recognition method for cabbages and weeds based on PCA and SIMCA","volume":"33","author":"Zu","year":"2013","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.patrec.2016.01.012","article-title":"Optimized projection for Collaborative Representation based Classification and its applications to face recognition","volume":"73","author":"Yin","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/TGRS.2014.2343955","article-title":"Collaborative representation for hyperspectral anomaly detection","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s00138-017-0821-y","article-title":"Collaborative representation with HM-LBP features for palmprint recognition","volume":"28","author":"Guo","year":"2017","journal-title":"Mach. Vis. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.ins.2017.12.014","article-title":"Robust face recognition via hierarchical collaborative representation","volume":"432","author":"Vo","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.1109\/TPAMI.2012.30","article-title":"Extended SRC: Undersampled face recognition via intraclass variant dictionary","volume":"34","author":"Deng","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intel."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1109\/TIP.2017.2675341","article-title":"Semi-Supervised sparse representation based classification for face recognition with insufficient labeled samples","volume":"26","author":"Gao","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","first-page":"1357","article-title":"Identification of cucumber disease using hyperspectral imaging and discriminate analysis","volume":"30","author":"Chai","year":"2010","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103996","DOI":"10.1016\/j.chemolab.2020.103996","article-title":"A deep learning based regression method on hyperspectral data for rapid prediction of cadmium residue in lettuce leaves","volume":"200","author":"Zhou","year":"2020","journal-title":"Chemom. Intel. Lab. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/4045\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:50:14Z","timestamp":1760176214000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/4045"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,21]]},"references-count":33,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20144045"],"URL":"https:\/\/doi.org\/10.3390\/s20144045","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,21]]}}}