{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T20:39:24Z","timestamp":1775421564186,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031314162","type":"print"},{"value":"9783031314179","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-31417-9_17","type":"book-chapter","created":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T12:02:31Z","timestamp":1683374551000},"page":"216-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Curated Dataset for Spinach Species Identification"],"prefix":"10.1007","author":[{"given":"R.","family":"Ahila Priyadharshini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Arivazhagan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Arun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,7]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","unstructured":"Singh, A., et al.: Indian spinach: an underutilized perennial leafy vegetable for nutritional security in developing world. Energy Ecol. Environ. 3(3), 195\u2013205 (2018). https:\/\/doi.org\/10.1007\/s40974-018-0091-1","DOI":"10.1007\/s40974-018-0091-1"},{"key":"17_CR2","unstructured":"Im, C., Nishida, H., Kunii, T. L : A hierarchical method of recognizing plant species by leaf shapes, In: Proc. IAPR Workshop Mach. Vis. Appl., pp. 158\u2013161 (1998)"},{"key":"17_CR3","unstructured":"Kulkarni, A.H., Rai, H.M., Jahagirdar, K.A., Upparamani, P.S.: \u1fbdA leaf recognition technique for plant classification using RBPNN and Zernike moments\u1fbd. Int. J. Adv. Res. Comput. Commun. Eng. 2(1), 984\u2013988 (2013)"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Prasvita, D.S., Herdiyeni, Y.: MedLeaf: mobile application for medicinal plant identification based on leaf image, Int. J. Adv. Sci., Eng. Inf. Technol., 3(2), pp. 5\u20138, (2013)","DOI":"10.18517\/ijaseit.3.2.287"},{"key":"17_CR5","unstructured":"Ekshinge, S., Sambhaji, I., Andore, M.D.: \u1fbdLeaf recognition algorithm using neural network based image processing\u1fbd. Asian J. Eng. Technol. Innov. 2(2), 10\u201316 (2014)"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Neto, J.C., Meyer, G.E., Jones, D.D., Samal, A.K.: Plant species identification using Elliptic Fourier leaf shape analysis. Comput. Electron. Agricult. 50(2), 121\u2013134 (2006)","DOI":"10.1016\/j.compag.2005.09.004"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Priya, C.A., Balasaravanan, T., Thanamani, A.S.: An efficient leaf recognition algorithm for plant classification using support vector machine In: Proc. Int. Conf. Pattern Recogn. Inform. Med. Eng. (PRIME), pp. 428\u2013432(2012)","DOI":"10.1109\/ICPRIME.2012.6208384"},{"key":"17_CR8","unstructured":"Zulkifli, Z.: Plant leaf identification using moment invariants & general regression neural network, M.S. thesis, Univ. Teknologi Malaysia, Johor Bahru, Malaysia (2009)"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Anami, B.S., Nandyal, S.S., Govardhan, A.: A combined color, texture and edge features based approach for identification and classification of Indian medicinal plants. Int. J. Comput. Appl. 6(12), 45\u201351 (2010)","DOI":"10.5120\/1122-1471"},{"key":"17_CR10","unstructured":"Bama, B.S., Valli, S.M., Raju, S., Kumar, V.A.: Content based leaf image retrieval (CBLIR) using shape, color and texture features. Indian J. Comput. Sci. Eng. 2(2), 202\u2013211 (2011)"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Man, Q.-K., Zheng, C.-H., Wang, X.-F., Lin, F.-Y.: Recognition of plant leaves using support vector machine, In: Proc. Int. Conf. Intell. Comput., pp. 192\u2013199 (2008)","DOI":"10.1007\/978-3-540-85930-7_26"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Chaki, J., Parekh, R.: Plant leaf recognition using shape based features and neural network classifiers. Int. J. Adv. Comput. Sci. Appl. 2(10), 41\u201347 (2011)","DOI":"10.14569\/IJACSA.2011.021007"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, H., Yanne, P., Liang, S.: Plant species classification using leaf shape and texture. Amer. J. Eng. Technol. Res., vol. 11(9), pp. 2025\u20132028 (2011)","DOI":"10.1109\/ICICEE.2012.538"},{"key":"17_CR14","doi-asserted-by":"crossref","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)","DOI":"10.1007\/s10586-017-0859-7"},{"key":"17_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/978-3-319-70353-4_52","volume-title":"Advanced Concepts for Intelligent Vision Systems","author":"P Pawara","year":"2017","unstructured":"Pawara, P., Okafor, E., Schomaker, L., Wiering, M.: Data Augmentation for Plant Classification. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2017. LNCS, vol. 10617, pp. 615\u2013626. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70353-4_52"},{"key":"17_CR16","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 Networksfor Plant Recognition. In: Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods. ICPRAM, pp. 479\u2013486 (2017)","DOI":"10.5220\/0006196204790486"},{"key":"17_CR17","doi-asserted-by":"publisher","unstructured":"Ahila Priyadharshini, R., Arivazhagan, S., Arun, M., Mirnalini, A.: Maize leaf disease classification using deep convolutional neural networks. Neural Comput. Appl. 31(12), 8887\u20138895 (2019). https:\/\/doi.org\/10.1007\/s00521-019-04228-3","DOI":"10.1007\/s00521-019-04228-3"},{"key":"17_CR18","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1007\/978-981-16-1092-9_23","volume-title":"Computer Vision and Image Processing","author":"R Ahila Priyadharshini","year":"2021","unstructured":"Ahila Priyadharshini, R., Arivazhagan, S., Arun, M.: Ayurvedic Medicinal Plants Identification: A Comparative Study on Feature Extraction Methods. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds.) CVIP 2020. CCIS, vol. 1377, pp. 268\u2013280. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-16-1092-9_23"},{"key":"17_CR19","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-85365-5_1","volume-title":"Advances in Deep Learning, Artificial Intelligence and Robotics","author":"M Islam","year":"2022","unstructured":"Islam, M., Ria, N.J., Ani, J.F., Masum, A.K.M., Abujar, S., Hossain, S.A.: Deep Learning Based Classification System for Recognizing Local Spinach. In: Troiano, L., et al. (eds.) Advances in Deep Learning, Artificial Intelligence and Robotics. LNNS, vol. 249, pp. 1\u201314. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-85365-5_1"},{"key":"17_CR20","doi-asserted-by":"publisher","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. Nat Methods 13, 35 (2017). https:\/\/doi.org\/10.1038\/nmeth.3707","DOI":"10.1038\/nmeth.3707"},{"key":"17_CR21","unstructured":"Lee, C-Y, Gallagher, PW, Tu, Z.: Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree. In: Artificial Intelligence and Statistics, pp 464\u2013472 (2016)"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L-C.:MobileNetV2: inverted residuals and linear bottlenecks, EEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31417-9_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T22:46:21Z","timestamp":1729377981000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31417-9_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031314162","9783031314179"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31417-9_17","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"7 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nagpur","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/vnit.ac.in\/cvip2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"110","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":"11","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":"36% - 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":"4","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)"}}]}}