{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T21:16:31Z","timestamp":1768338991274,"version":"3.49.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031554858","type":"print"},{"value":"9783031554865","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-55486-5_8","type":"book-chapter","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T07:02:33Z","timestamp":1709708553000},"page":"97-110","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Benchmarking ML and DL Models for Mango Leaf Disease Detection: A Comparative Analysis"],"prefix":"10.1007","author":[{"given":"Hritwik","family":"Ghosh","sequence":"first","affiliation":[]},{"given":"Irfan Sadiq","family":"Rahat","sequence":"additional","affiliation":[]},{"given":"Rasmita","family":"Lenka","sequence":"additional","affiliation":[]},{"given":"Sachi Nandan","family":"Mohanty","sequence":"additional","affiliation":[]},{"given":"Deepak","family":"Chauhan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,7]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Maheshwari, K.: Performance analysis of mango leaf disease using machine learning technique. Int. J. Res. Appl. Sci. Eng. Technol. 9(1), 856\u2013862 (2021). https:\/\/doi.org\/10.22214\/ijraset.2021.32926","DOI":"10.22214\/ijraset.2021.32926"},{"issue":"3","key":"8_CR2","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s42044-020-00057-z","volume":"3","author":"MR Mia","year":"2020","unstructured":"Mia, M.R., Roy, S., Das, S.K., Rahman, M.A.: Mango leaf disease recognition using neural network and support vector machine. Iran J. Comput. Sci. (Online) 3(3), 185\u2013193 (2020). https:\/\/doi.org\/10.1007\/s42044-020-00057-z","journal-title":"Iran J. Comput. Sci. (Online)"},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"SivaramKrishnan, M., et al.: Leaf disease identification using machine learning models. AIP Conf. Proc. 2519(1) (2022). https:\/\/doi.org\/10.1063\/5.0109675","DOI":"10.1063\/5.0109675"},{"key":"8_CR4","doi-asserted-by":"publisher","unstructured":"Tulshan, A.S., Raul, N.: Plant leaf disease detection using machine learning. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1\u20136 (2019). https:\/\/doi.org\/10.1109\/ICCCNT45670.2019.8944556","DOI":"10.1109\/ICCCNT45670.2019.8944556"},{"key":"8_CR5","doi-asserted-by":"publisher","unstructured":"Sanath Rao, U., et al.: Deep learning precision farming: grapes and mango leaf disease detection by transfer learning. Global Trans. Proc. 2(2), 535\u2013544 (2021). https:\/\/doi.org\/10.1016\/j.gltp.2021.08.002","DOI":"10.1016\/j.gltp.2021.08.002"},{"key":"8_CR6","doi-asserted-by":"publisher","unstructured":"Deep learning for image based mango leaf disease detection. Int. J. Recent Technol. Eng. 8(3S3), 54\u201356 (2019). https:\/\/doi.org\/10.35940\/ijrte.C1030.1183S319","DOI":"10.35940\/ijrte.C1030.1183S319"},{"key":"8_CR7","doi-asserted-by":"publisher","unstructured":"Selvakumar, A., Balasundaram, A.: Automated mango leaf infection classification using weighted and deep features with optimized recurrent neural network concept. Imaging Sci. J. ahead-of-print (ahead-of-print), 1\u201319 (2023). https:\/\/doi.org\/10.1080\/13682199.2023.2204036","DOI":"10.1080\/13682199.2023.2204036"},{"issue":"24","key":"8_CR8","doi-asserted-by":"publisher","first-page":"11901","DOI":"10.3390\/app112411901","volume":"11","author":"R Saleem","year":"2021","unstructured":"Saleem, R., Shah, J.H., Sharif, M., Yasmin, M., Yong, H.-S., Cha, J.: Mango leaf disease recognition and classification using novel segmentation and vein pattern technique. Appl. Sci. 11(24), 11901 (2021). https:\/\/doi.org\/10.3390\/app112411901","journal-title":"Appl. Sci."},{"key":"8_CR9","series-title":"Lecture Notes in Computational Vision and Biomechanics","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1007\/978-3-319-71767-8_35","volume-title":"Computational Vision and Bio Inspired Computing","author":"K Srunitha","year":"2018","unstructured":"Srunitha, K., Bharathi, D.: Mango leaf unhealthy region detection and classification. In: Hemanth, D.J., Smys, S. (eds.) Computational Vision and Bio Inspired Computing. LNCVB, vol. 28, pp. 422\u2013436. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-71767-8_35"},{"key":"8_CR10","doi-asserted-by":"publisher","unstructured":"Saleem, R., Shah, J.H., Sharif, M., Ansari, G.J.: Mango leaf disease identification using fully resolution convolutional network. Comput. Mater. Continua 69(3), 3581 (2021). https:\/\/doi.org\/10.32604\/cmc.2021.017700","DOI":"10.32604\/cmc.2021.017700"},{"key":"8_CR11","doi-asserted-by":"publisher","unstructured":"Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N.: Water quality assessment through predictive machine learning. In: Balas, V.E., Semwal, V.B., Khandare, A. (eds.) Intelligent Computing and Networking. IC-ICN 2023. LNNS, vol. 699, pp. 77\u201388. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-99-3177-4_6","DOI":"10.1007\/978-981-99-3177-4_6"},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Xie, X., Wang, J., Hu, Z., Zhao, Y.: Intelligent detection of mango disease spores based on mask scoring R-CNN. In: 2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT), pp. 768\u2013774 (2021). https:\/\/doi.org\/10.1109\/ACAIT53529.2021.9731325","DOI":"10.1109\/ACAIT53529.2021.9731325"},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"43721","DOI":"10.1109\/ACCESS.2019.2907383","volume":"7","author":"UP Singh","year":"2019","unstructured":"Singh, U.P., Chouhan, S.S., Jain, S., Jain, S.: Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access 7, 43721\u201343729 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2907383","journal-title":"IEEE Access"},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"189960","DOI":"10.1109\/ACCESS.2020.3031914","volume":"8","author":"TN Pham","year":"2020","unstructured":"Pham, T.N., Tran, L.V., Dao, S.V.T.: Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection. IEEE Access 8, 189960\u2013189973 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3031914","journal-title":"IEEE Access"},{"key":"8_CR15","doi-asserted-by":"publisher","unstructured":"Manoharan, N., Thomas, V.J., Anto Sahaya Dhas, D.: Identification of mango leaf disease using deep learning. In: 2021 Asian Conference on Innovation in Technology (ASIANCON), pp. 1\u20138 (2021). https:\/\/doi.org\/10.1109\/ASIANCON51346.2021.9544689","DOI":"10.1109\/ASIANCON51346.2021.9544689"},{"key":"8_CR16","doi-asserted-by":"publisher","unstructured":"Pruvost, O., Savelon, C., Boyer, C., Chiroleu, F., Gagnevin, L., Jacques, M.-A.: Populations of Xanthomonas citri pv. mangiferaeindicae from asymptomatic mango leaves are primarily endophytic. Microbial. Ecol. 58(1), 170\u2013178 (2009). https:\/\/doi.org\/10.1007\/s00248-008-9480-x","DOI":"10.1007\/s00248-008-9480-x"},{"issue":"3","key":"8_CR17","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1094\/PDIS-94-3-0380C","volume":"94","author":"L Baeza-Montanez","year":"2010","unstructured":"Baeza-Montanez, L., Gomez-Cabrera, R., Garcia-Pedrajas, M.: First report of verticillium wilt caused by verticillium Dahliae on mango trees (Mangifera indica) in Southern Spain. Plant Dis. 94(3), 380 (2010). https:\/\/doi.org\/10.1094\/PDIS-94-3-0380C","journal-title":"Plant Dis."},{"key":"8_CR18","doi-asserted-by":"publisher","unstructured":"Augustyn, W.A., Regnier, T., Combrinck, S., Botha, B.M.: Metabolic profiling of mango cultivars to identify biomarkers for resistance against Fusarium infection. Phytochem. Lett. 10, civ\u2013cx (2014). https:\/\/doi.org\/10.1016\/j.phytol.2014.05.014","DOI":"10.1016\/j.phytol.2014.05.014"}],"container-title":["Communications in Computer and Information Science","Applied Machine Learning and Data Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-55486-5_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T07:10:26Z","timestamp":1709709026000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-55486-5_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031554858","9783031554865"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-55486-5_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"7 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AMLDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Machine Learning and Data Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"L\u00fcbeck","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"amlda2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icamlda.org\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"76","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":"17","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":"2","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":"22% - 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":"2","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}