{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:04:28Z","timestamp":1774631068849,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031213847","type":"print"},{"value":"9783031213854","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-21385-4_23","type":"book-chapter","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T05:03:52Z","timestamp":1670907832000},"page":"263-278","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Apple Leaf Diseases Detection System: A Review of the Different Segmentation and Deep Learning Methods"],"prefix":"10.1007","author":[{"given":"Anupam","family":"Bonkra","sequence":"first","affiliation":[]},{"given":"Ajit","family":"Noonia","sequence":"additional","affiliation":[]},{"given":"Amandeep","family":"Kaur","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"issue":"1","key":"23_CR1","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/sym10010011","volume":"10","author":"B Liu","year":"2018","unstructured":"Liu, B., Zhang, Y., He, D., Li, Y.: Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10(1), 11 (2018)","journal-title":"Symmetry"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Fang, T., Chen, P., Zhang, J., Wang, B.: Identification of apple leaf diseases based on convolutional neural network. In\u00a0International Conference on Intelligent Computing, August, pp. 553\u2013564. Springer, Cham (2019)","DOI":"10.1007\/978-3-030-26763-6_53"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Srinidhi, V.V., Sahay, A., Deeba, K.: Plant pathology disease detection in apple leaves using deep convolutional neural networks: Apple leaves disease detection using EfficientNet and DenseNet. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), April, pp. 1119\u20131127. IEEE (2021)","DOI":"10.1109\/ICCMC51019.2021.9418268"},{"issue":"1","key":"23_CR4","first-page":"1","volume":"10","author":"AI Khan","year":"2021","unstructured":"Khan, A.I., Quadri, S.M.K., Banday, S.: Deep learning for apple diseases: classification and identification. Int. J. Comput. Intell. Stud. 10(1), 1\u201312 (2021)","journal-title":"Int. J. Comput. Intell. Stud."},{"issue":"7","key":"23_CR5","doi-asserted-by":"publisher","first-page":"617","DOI":"10.3390\/agriculture11070617","volume":"11","author":"P Bansal","year":"2021","unstructured":"Bansal, P., Kumar, R., Kumar, S.: Disease detection in apple leaves using deep convolutional neural network. Agriculture 11(7), 617 (2021)","journal-title":"Agriculture"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Sun, H., Xu, H., Liu, B., He, D., He, J., Zhang, H., Geng, N.: MEAN-SSD: a novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks.\u00a0Comput. Electron. Agric. 189, 106379 (2021)","DOI":"10.1016\/j.compag.2021.106379"},{"issue":"7","key":"23_CR7","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.3390\/sym12071065","volume":"12","author":"X Chao","year":"2020","unstructured":"Chao, X., Sun, G., Zhao, H., Li, M., He, D.: Identification of apple tree leaf diseases based on deep learning models. Symmetry 12(7), 1065 (2020)","journal-title":"Symmetry"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Zhao, M.: Research on deep learning in apple leaf disease recognition.\u00a0Comput. Electron. Agric. 168, 105146 (2020)","DOI":"10.1016\/j.compag.2019.105146"},{"key":"23_CR9","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.compag.2017.09.038","volume":"148","author":"M Shuaibu","year":"2018","unstructured":"Shuaibu, M., Lee, W.S., Schueller, J., Gader, P., Hong, Y.K., Kim, S.: Unsupervised hyperspectral band selection for apple Marssonina blotch detection. Comput. Electron. Agric. 148, 45\u201353 (2018)","journal-title":"Comput. Electron. Agric."},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Chandel, A.K., Khot, L.R., Sallato, B.: Apple powdery mildew infestation detection and mapping using high-resolution visible and multispectral aerial imaging technique.\u00a0Sci. Hortic. 287, 110228 (2021)","DOI":"10.1016\/j.scienta.2021.110228"},{"key":"23_CR11","doi-asserted-by":"publisher","first-page":"576","DOI":"10.3389\/fpls.2019.00576","volume":"10","author":"S Jarolmasjed","year":"2019","unstructured":"Jarolmasjed, S., et al.: High-throughput phenotyping of fire blight disease symptoms using sensing techniques in apple. Front. Plant Sci. 10, 576 (2019)","journal-title":"Front. Plant Sci."},{"key":"23_CR12","unstructured":"Kodors, S., Lacis, G., Sokolova, O., Zhukovs, V., Apeinans, I., Bartulsons, T.: Apple scab detection using CNN and transfer learning (2021)"},{"issue":"3","key":"23_CR13","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1080\/07060661.2019.1610070","volume":"41","author":"PA Abbasi","year":"2019","unstructured":"Abbasi, P.A., Ali, S., Braun, G., Bevis, E., Fillmore, S.: Reducing apple scab and frogeye or black rot infections with salicylic acid or its analogue on field-established apple trees. Can. J. Plant Path. 41(3), 345\u2013354 (2019)","journal-title":"Can. J. Plant Path."},{"key":"23_CR14","doi-asserted-by":"publisher","first-page":"59069","DOI":"10.1109\/ACCESS.2019.2914929","volume":"7","author":"P Jiang","year":"2019","unstructured":"Jiang, P., Chen, Y., Liu, B., He, D., Liang, C.: Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 7, 59069\u201359080 (2019)","journal-title":"IEEE Access"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Thapa, R., Snavely, N., Belongie, S., Khan, A.: The plant pathology 2020 challenge dataset to classify foliar disease of apples.\u00a0arXiv preprint.\u00a0arXiv:2004.11958\u00a0(2020)","DOI":"10.1002\/aps3.11390"},{"issue":"12","key":"23_CR16","doi-asserted-by":"publisher","first-page":"3535","DOI":"10.3390\/s20123535","volume":"20","author":"Q Yan","year":"2020","unstructured":"Yan, Q., Yang, B., Wang, W., Wang, B., Chen, P., Zhang, J.: Apple leaf diseases recognition based on an improved convolutional neural network. Sensors 20(12), 3535 (2020)","journal-title":"Sensors"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Baranwal, S., Khandelwal, S., Arora, A.: Deep learning convolutional neural network for apple leaves disease detection. In: Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), February, Amity University Rajasthan, Jaipur, India (2019)","DOI":"10.2139\/ssrn.3351641"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Noon, S.K., Amjad, M., Qureshi, M.A., Mannan, A.: Use of deep learning techniques for identification of plant leaf stresses: A review.\u00a0Sustain. Comput.: Inform. Syst. 100443 (2020)","DOI":"10.1016\/j.suscom.2020.100443"},{"issue":"11","key":"23_CR20","doi-asserted-by":"publisher","first-page":"1721","DOI":"10.3390\/agronomy10111721","volume":"10","author":"K Yang","year":"2020","unstructured":"Yang, K., Zhong, W., Li, F.: Leaf segmentation and classification with a complicated background using deep learning. Agronomy 10(11), 1721 (2020)","journal-title":"Agronomy"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Mathew, M.P., Mahesh, T.Y.: Determining the region of apple leaf affected by disease using YOLO V3. In: 2021 International Conference on Communication, Control and Information Sciences (ICCISc), June, vol. 1, pp. 1\u20134. IEEE (2021)","DOI":"10.1109\/ICCISc52257.2021.9484876"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: single shot multibox detector. In:\u00a0European Conference on Computer Vision, October, pp. 21\u201337. Springer, Cham (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Hussain, M., Bird, J.J., Faria, D.R.: A study on cnn transfer learning for image classification. In: UK Workshop on Computational Intelligence, September, pp. 191\u2013202. Springer, Cham (2018)","DOI":"10.1007\/978-3-319-97982-3_16"}],"updated-by":[{"DOI":"10.1007\/978-3-031-21385-4_44","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000}}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21385-4_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T15:27:31Z","timestamp":1691594851000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21385-4_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031213847","9783031213854"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21385-4_23","relation":{"correction":[{"id-type":"doi","id":"10.1007\/978-3-031-21385-4_44","asserted-by":"object"}]},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"10 August 2023","order":2,"name":"change_date","label":"Change Date","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"A correction has been published.","order":4,"name":"change_details","label":"Change Details","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hyderabad","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaids2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icaids.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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"195","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":"43","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":"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":"4","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)"}}]}}