{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:39:43Z","timestamp":1759333183750,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030757670"},{"type":"electronic","value":"9783030757687"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-75768-7_22","type":"book-chapter","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T15:04:20Z","timestamp":1620399860000},"page":"272-284","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Compressed and Accelerated SegNet for Plant Leaf Disease Segmentation: A Differential Evolution Based Approach"],"prefix":"10.1007","author":[{"given":"Mohit","family":"Agarwal","sequence":"first","affiliation":[]},{"given":"Suneet Kr.","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"K. K.","family":"Biswas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,8]]},"reference":[{"key":"22_CR1","unstructured":"Street scene images dataset (2007). http:\/\/mi.eng.cam.ac.uk\/research\/projects\/VideoRec\/CamSeq01\/"},{"key":"22_CR2","unstructured":"Keras segnet: simplified segnet model (2018). https:\/\/github.com\/imlab-uiip\/keras-segnet"},{"issue":"2","key":"22_CR3","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s41095-019-0139-y","volume":"5","author":"S Alqazzaz","year":"2019","unstructured":"Alqazzaz, S., Sun, X., Yang, X., Nokes, L.: Automated brain tumor segmentation on multi-modal MR image using SegNet. Comput. Visual Media 5(2), 209\u2013219 (2019)","journal-title":"Comput. Visual Media"},{"issue":"3","key":"22_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3005348","volume":"13","author":"S Anwar","year":"2017","unstructured":"Anwar, S., Hwang, K., Sung, W.: Structured pruning of deep convolutional neural networks. ACM J. Emerg. Technol. Comput. Syst. (JETC) 13(3), 1\u201318 (2017)","journal-title":"ACM J. Emerg. Technol. Comput. Syst. (JETC)"},{"issue":"12","key":"22_CR5","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"22_CR6","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","volume":"30","author":"GJ Brostow","year":"2009","unstructured":"Brostow, G.J., Fauqueur, J., Cipolla, R.: Semantic object classes in video: a high-definition ground truth database. Pattern Recogn. Lett. 30(2), 88\u201397 (2009)","journal-title":"Pattern Recogn. Lett."},{"key":"22_CR7","unstructured":"Cheng, Y., Wang, D., Zhou, P., Zhang, T.: A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282 (2017)"},{"key":"22_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-36896-2","volume-title":"Differential Evolution","author":"V Feoktistov","year":"2006","unstructured":"Feoktistov, V.: Differential Evolution. Springer, Boston (2006). https:\/\/doi.org\/10.1007\/978-0-387-36896-2"},{"issue":"30","key":"22_CR9","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.ifacol.2019.12.499","volume":"52","author":"P Ganesh","year":"2019","unstructured":"Ganesh, P., Volle, K., Burks, T., Mehta, S.: Deep orange: mask R-CNN based orange detection and segmentation. IFAC-PapersOnLine 52(30), 70\u201375 (2019)","journal-title":"IFAC-PapersOnLine"},{"key":"22_CR10","unstructured":"Gong, Y., Liu, L., Yang, M., Bourdev, L.: Compressing deep convolutional networks using vector quantization. arXiv preprint arXiv:1412.6115 (2014)"},{"key":"22_CR11","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)"},{"key":"22_CR12","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems, pp. 1135\u20131143 (2015)"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1389\u20131397 (2017)","DOI":"10.1109\/ICCV.2017.155"},{"key":"22_CR14","unstructured":"Hughes, D., Salath\u00e9, M., et al.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060 (2015)"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Islam, M., Dinh, A., Wahid, K., Bhowmik, P.: Detection of potato diseases using image segmentation and multiclass support vector machine. In: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1\u20134. IEEE (2017)","DOI":"10.1109\/CCECE.2017.7946594"},{"key":"22_CR16","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.compag.2017.04.013","volume":"138","author":"A Johannes","year":"2017","unstructured":"Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez-Vaamonde, S., Navajas, A.D., Ortiz-Barredo, A.: Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Comput. Electron. Agric. 138, 200\u2013209 (2017)","journal-title":"Comput. Electron. Agric."},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Lee, U., Chang, S., Putra, G.A., Kim, H., Kim, D.H.: An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis. PLoS ONE 13(4), (2018)","DOI":"10.1371\/journal.pone.0196615"},{"key":"22_CR18","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 (2016)"},{"key":"22_CR19","doi-asserted-by":"publisher","first-page":"155","DOI":"10.3389\/fpls.2019.00155","volume":"10","author":"K Lin","year":"2019","unstructured":"Lin, K., Gong, L., Huang, Y., Liu, C., Pan, J.: Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network. Front. Plant Sci. 10, 155 (2019)","journal-title":"Front. Plant Sci."},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2736\u20132744 (2017)","DOI":"10.1109\/ICCV.2017.298"},{"key":"22_CR21","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.compag.2018.08.048","volume":"154","author":"J Ma","year":"2018","unstructured":"Ma, J., Du, K., Zheng, F., Zhang, L., Gong, Z., Sun, Z.: A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput. Electron. Agric. 154, 18\u201324 (2018)","journal-title":"Comput. Electron. Agric."},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Manickam, R., Rajan, S.K., Subramanian, C., Xavi, A., Eanoch, G.J., Yesudhas, H.R.: Person identification with aerial imaginary using SegNet based semantic segmentation. Earth Sci. Inform. 1\u201312 (2020)","DOI":"10.1007\/s12145-020-00516-y"},{"key":"22_CR23","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"SP Mohanty","year":"2016","unstructured":"Mohanty, S.P., Hughes, D.P., Salath\u00e9, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)","journal-title":"Front. Plant Sci."},{"key":"22_CR24","unstructured":"Nguyen, H.D., Na, I.S., Kim, S.H.: Hand segmentation and fingertip tracking from depth camera images using deep convolutional neural network and multi-task SegNet. arXiv preprint arXiv:1901.03465 (2019)"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Song, S., Lichtenberg, S.P., Xiao, J.: Sun RGB-D: a RGB-D scene understanding benchmark suite. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 567\u2013576 (2015)","DOI":"10.1109\/CVPR.2015.7298655"},{"issue":"4","key":"22_CR26","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341\u2013359 (1997)","journal-title":"J. Global Optim."},{"key":"22_CR27","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.neucom.2018.09.038","volume":"323","author":"Q Zhang","year":"2019","unstructured":"Zhang, Q., Zhang, M., Chen, T., Sun, Z., Ma, Y., Yu, B.: Recent advances in convolutional neural network acceleration. Neurocomputing 323, 37\u201351 (2019)","journal-title":"Neurocomputing"},{"key":"22_CR28","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.compag.2019.04.014","volume":"162","author":"J Zhou","year":"2019","unstructured":"Zhou, J., Fu, X., Zhou, S., Zhou, J., Ye, H., Nguyen, H.T.: Automated segmentation of soybean plants from 3d point cloud using machine learning. Comput. Electron. Agric. 162, 143\u2013153 (2019)","journal-title":"Comput. Electron. Agric."}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75768-7_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T21:41:19Z","timestamp":1639086079000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75768-7_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030757670","9783030757687"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75768-7_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"8 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2021.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"673","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":"157","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":"23% - 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":"7","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)"}}]}}