{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:42:05Z","timestamp":1743057725688,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811391866"},{"type":"electronic","value":"9789811391873"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-981-13-9187-3_41","type":"book-chapter","created":{"date-parts":[[2019,7,16]],"date-time":"2019-07-16T05:06:16Z","timestamp":1563253576000},"page":"453-470","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation and Analysis of Plant Classification System Based on Feature Level Fusion and Score Level Fusion"],"prefix":"10.1007","author":[{"given":"Pradip","family":"Salve","sequence":"first","affiliation":[]},{"given":"Pravin","family":"Yannawar","sequence":"additional","affiliation":[]},{"given":"Milind","family":"Sardesai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,7,17]]},"reference":[{"issue":"13","key":"41_CR1","first-page":"5660","volume":"28","author":"BM Fern","year":"2017","unstructured":"Fern, B.M., et al.: Stratified classification of plant species based on venation state. Biomed. Res. 28(13), 5660\u20135663 (2017)","journal-title":"Biomed. Res."},{"key":"41_CR2","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/978-81-322-2517-1_10","volume-title":"Proceedings of the Second International Conference on Computer and Communication Technologies","author":"P Salve","year":"2016","unstructured":"Salve, P., Sardesai, M., Manza, R., Yannawar, P.: Identification of the plants based on leaf shape descriptors. In: Satapathy, S.C., Raju, K.S., Mandal, J.K., Bhateja, V. (eds.) Proceedings of the Second International Conference on Computer and Communication Technologies. AISC, vol. 379, pp. 85\u2013101. Springer, New Delhi (2016). \n                    https:\/\/doi.org\/10.1007\/978-81-322-2517-1_10"},{"key":"41_CR3","series-title":"Multimedia Systems and Applications","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/978-3-319-76445-0_8","volume-title":"Multimedia Tools and Applications for Environmental & Biodiversity Informatics","author":"P Bonnet","year":"2018","unstructured":"Bonnet, P., et al.: Plant identification: experts vs. machines in the era of deep learning. In: Joly, A., Vrochidis, S., Karatzas, K., Karppinen, A., Bonnet, P. (eds.) Multimedia Tools and Applications for Environmental & Biodiversity Informatics. MMSA, vol. 379, pp. 131\u2013149. Springer, Cham (2018). \n                    https:\/\/doi.org\/10.1007\/978-3-319-76445-0_8"},{"issue":"1","key":"41_CR4","first-page":"30","volume":"5","author":"M Amlekar","year":"2016","unstructured":"Amlekar, M., Manza, R.R., Yannawar, P., Gaikwad, A.T.: Plant classification based on leaf features. IBMRD\u2019s J. Manag. Res. 5(1), 30\u201334 (2016)","journal-title":"IBMRD\u2019s J. Manag. Res."},{"key":"41_CR5","unstructured":"Amlekar, M.M., Ashok T.G.: Plant classification based on leaf Shape features using Neural Network. Int. J. Adv. Res. Sci. Eng. 635\u2013639 (2017)"},{"key":"41_CR6","unstructured":"Rahmadhani, M., Herdiyeni, Y.: Shape and vein extraction on plant leaf images using Fourier and B-spline modeling. In: AFITA International Conference, the Quality Information for Competitive Agricultural Based Production System and Commerce, pp. 306\u2013310 (2010)"},{"key":"41_CR7","doi-asserted-by":"crossref","unstructured":"Sun, Z., Lu, S., Guo, X., Tian, Y.: Leaf vein and contour extraction from point cloud data. In: 2011 International Conference on Virtual Reality and Visualization (ICVRV), pp. 11\u201316. IEEE (2011)","DOI":"10.1109\/ICVRV.2011.40"},{"key":"41_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/11919629_44","volume-title":"Advances in Visual Computing","author":"J Clarke","year":"2006","unstructured":"Clarke, J., et al.: Venation pattern analysis of leaf images. In: Bebis, G., et al. (eds.) ISVC 2006. LNCS, vol. 4292, pp. 427\u2013436. Springer, Heidelberg (2006). \n                    https:\/\/doi.org\/10.1007\/11919629_44"},{"key":"41_CR9","doi-asserted-by":"publisher","unstructured":"Siravenha, A.C., Carvalho, S.R.: Plant classification from leaf textures. In: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, QLD, pp. 1\u20138 (2016). \n                    https:\/\/doi.org\/10.1109\/DICTA.2016.7797073","DOI":"10.1109\/DICTA.2016.7797073"},{"key":"41_CR10","unstructured":"Goeau, H., Bonnet, P., Joly, A.: Plant identification based on noisy web data: the amazing performance of deep learning. In: CLEF 2017-Conference and Labs of the Evaluation Forum (LifeCLEF 2017) (2017)"},{"key":"41_CR11","doi-asserted-by":"crossref","unstructured":"Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y., Chang, Y., Xiang, Q.: A leaf recognition algorithm for plant classification using probabilistic neural network. In: IEEE 7th International Symposium on Signal Processing and Information Technology, Cairo, Egypt (2007)","DOI":"10.1109\/ISSPIT.2007.4458016"},{"issue":"5","key":"41_CR12","doi-asserted-by":"publisher","first-page":"6915","DOI":"10.1007\/s11042-016-3309-2","volume":"76","author":"S Prasad","year":"2017","unstructured":"Prasad, S., Kumar, P.S., Ghosh, D.: An efficient low vision plant leaf shape identification system for smart phones. Multimed. Tools Appl. 76(5), 6915\u20136939 (2017)","journal-title":"Multimed. Tools Appl."},{"key":"41_CR13","doi-asserted-by":"publisher","unstructured":"Chaki, J., Parekh, R., Bhattacharya, S.: Plant leaf classification using multiple descriptors: a hierarchical approach. J. King Saud Univ.-Comput. Inf. Sci. (2018), ISSN 1319-1578. \n                    https:\/\/doi.org\/10.1016\/j.jksuci.2018.01.007","DOI":"10.1016\/j.jksuci.2018.01.007"},{"key":"41_CR14","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/978-3-319-89743-1_51","volume-title":"Computational Intelligence and Its Applications","author":"L Hamrouni","year":"2018","unstructured":"Hamrouni, L., Bensaci, R., Kherfi, M.L., Khaldi, B., Aiadi, O.: Automatic recognition of plant leaves using parallel combination of classifiers. In: Amine, A., Mouhoub, M., Ait Mohamed, O., Djebbar, B. (eds.) CIIA 2018. IAICT, vol. 522, pp. 597\u2013606. Springer, Cham (2018). \n                    https:\/\/doi.org\/10.1007\/978-3-319-89743-1_51"},{"key":"41_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2017.05.015","volume":"71","author":"SH Lee","year":"2017","unstructured":"Lee, S.H., Chee, S.C., Simon, J.M., Remagnino, P.: How deep learning extracts and learns leaf features for plant classification. Pattern Recogn. 71, 1\u201313 (2017)","journal-title":"Pattern Recogn."},{"issue":"2","key":"41_CR16","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1007\/s10586-017-0859-7","volume":"20","author":"S Zhang","year":"2017","unstructured":"Zhang, S., Wang, H., Huang, W.: Two-stage plant species recognition by local mean clustering and Weighted sparse representation classification. Cluster Comput. 20(2), 1517\u20131525 (2017)","journal-title":"Cluster Comput."},{"key":"41_CR17","doi-asserted-by":"publisher","first-page":"e3792","DOI":"10.7717\/peerj.3792","volume":"5","author":"M Murat","year":"2017","unstructured":"Murat, M., Chang, S.-W., Abu, A., Yap, H.J., Yong, K.-T.: Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach. PeerJ 5, e3792 (2017)","journal-title":"PeerJ"},{"key":"41_CR18","doi-asserted-by":"crossref","unstructured":"Pawara, P., Okafor, E., Surinta, O., Schomaker, L., Wiering, M.: Comparing local descriptors and bags of visual words to deep convolutional neural networks for plant recognition. In: ICPRAM, pp. 479\u2013486 (2017)","DOI":"10.5220\/0006196204790486"},{"key":"41_CR19","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.neucom.2017.01.018","volume":"235","author":"MM Ghazi","year":"2017","unstructured":"Ghazi, M.M., Yanikoglu, B., Aptoula, E.: Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235, 228\u2013235 (2017)","journal-title":"Neurocomputing"},{"key":"41_CR20","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.ecoinf.2017.05.005","volume":"40","author":"P Barre","year":"2017","unstructured":"Barre, P., St\u0151ver, B.C., M\u00fcller, K.F., Steinhage, V.: LeafNet: a computer vision system for automatic plant species identification. Ecol. Inf. 40, 50\u201356 (2017)","journal-title":"Ecol. Inf."},{"key":"41_CR21","unstructured":"Goeau, H., Bonnet, P., Joly, A.: Plant identification based on noisy web data: the amazing performance of deep learning. In: CLEF 2017-Conference and Labs of the Evaluation Forum (LifeCLEF 2017), pp. 1\u201313 (2017)"},{"key":"41_CR22","unstructured":"Lasseck, M.: Image-based plant species identification with deep convolutional neural networks. In: Working Notes of CLEF 2017 (2017)"},{"key":"41_CR23","doi-asserted-by":"crossref","unstructured":"Santosh, K.C., Antani, S., Thoma, G.: Stitched multipanel biomedical figure separation. In: 2015 IEEE 28th International Symposium on Computer-Based Medical Systems (CBMS), pp. 54\u201359. IEEE (2015)","DOI":"10.1109\/CBMS.2015.51"},{"issue":"3","key":"41_CR24","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/MIS.2016.24","volume":"31","author":"KC Santosh","year":"2016","unstructured":"Santosh, K.C., Wendling, L., Antani, S., Thoma, G.R.: Overlaid arrow detection for labeling regions of interest in biomedical images. IEEE Intell. Syst. 31(3), 66\u201375 (2016)","journal-title":"IEEE Intell. Syst."},{"key":"41_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.imavis.2015.06.010","volume":"42","author":"S Candemir","year":"2015","unstructured":"Candemir, S., Borovikov, E., Santosh, K.C., Antani, S., Thoma, G.: RSILC: rotation-and scale-invariant, line-based color-aware descriptor. Image Vis. Comput. 42, 1\u201312 (2015)","journal-title":"Image Vis. Comput."},{"key":"41_CR26","doi-asserted-by":"crossref","unstructured":"Fouad, M.M.M., Zawbaa, H.M., El-Bendary, N., Hassanien, A.E.: Automatic Nile Tilapia fish classification approach using machine learning techniques. In: 2013 13th International Conference on Hybrid Intelligent Systems (HIS), pp. 173\u2013178. IEEE (2013)","DOI":"10.1109\/HIS.2013.6920477"},{"key":"41_CR27","unstructured":"Mistry, D., Banerjee, A.: Comparison of feature detection and matching approaches: SIFT and SURF. GRD J.- Global Res. Dev. J. Eng. 2(4), 7\u201313 (2017), ISSN 2455-5703"},{"issue":"3","key":"41_CR28","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","volume":"110","author":"B Herbert","year":"2008","unstructured":"Herbert, B., Andreas, E., Tinne, T., Luc, V.G.: Speeded up robust feature (SURF). J. Comput. Vis. Image Underst. 110(3), 346\u2013359 (2008)","journal-title":"J. Comput. Vis. Image Underst."},{"issue":"1","key":"41_CR29","first-page":"8","volume":"1","author":"S Utsav","year":"2014","unstructured":"Utsav, S., Darshana, M., Asim, B.: Image registration of multi-view satellite images using best feature points detection and matching methods from SURF. SIFT PCA-SIFT 1(1), 8\u201318 (2014)","journal-title":"SIFT PCA-SIFT"},{"issue":"3","key":"41_CR30","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","volume":"110","author":"H Bay","year":"2008","unstructured":"Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: SURF: speeded up robust features. Comput. Vis. Image Underst. (CVIU) 110(3), 346\u2013359 (2008)","journal-title":"Comput. Vis. Image Underst. (CVIU)"},{"key":"41_CR31","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.procs.2015.04.002","volume":"50","author":"V Vijayakumar","year":"2015","unstructured":"Vijayakumar, V., Neelanarayanan, V., Veeramuthu, A., Meenakshi, S., PriyaDarsini, V.: Big data, cloud and computing challengesbrain image classification using learning machine approach and brain structure analysis. Proc. Comput. Sci. 50, 388\u2013394 (2015)","journal-title":"Proc. Comput. Sci."},{"issue":"6","key":"41_CR32","doi-asserted-by":"publisher","first-page":"2926","DOI":"10.21817\/ijet\/2016\/v8i6\/160806254","volume":"8","author":"KPS Shijin","year":"2016","unstructured":"Shijin, K.P.S., Dharun, V.S.: Extraction of texture features using GLCM and shape features using connected regions. Int. J. Eng. Technol. (IJET) 8(6), 2926\u20132930 (2016)","journal-title":"Int. J. Eng. Technol. (IJET)"},{"key":"41_CR33","doi-asserted-by":"publisher","unstructured":"Salve, P., Yannawar, P., Sardesai, M.: Multimodal plant recognition through hybrid feature fusion technique using imaging and non-imaging hyper-spectral data. J. King Saud Univ. - Comput. Inf. Sci. (2018), ISSN 1319-1578. \n                    https:\/\/doi.org\/10.1016\/j.jksuci.2018.09.018","DOI":"10.1016\/j.jksuci.2018.09.018"},{"key":"41_CR34","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-981-13-5758-9_2","volume-title":"Advances in Signal Processing and Intelligent Recognition Systems","author":"P Salve","year":"2019","unstructured":"Salve, P., Sardesai, M., Yannawar, P.: Classification of plants using GIST and LBP score level fusion. In: Thampi, S.M., Marques, O., Krishnan, S., Li, K.-C., Ciuonzo, D., Kolekar, M.H. (eds.) SIRS 2018. CCIS, vol. 968, pp. 15\u201329. Springer, Singapore (2019). \n                    https:\/\/doi.org\/10.1007\/978-981-13-5758-9_2"}],"container-title":["Communications in Computer and Information Science","Recent Trends in Image Processing and Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-13-9187-3_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,16]],"date-time":"2019-08-16T00:06:21Z","timestamp":1565913981000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-13-9187-3_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9789811391866","9789811391873"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-13-9187-3_41","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"17 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RTIP2R","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Recent Trends in Image Processing and Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Solapur","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rtip2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/rtip2r.org\/2018\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"374","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"173","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"-","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}