{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T06:47:39Z","timestamp":1742971659099,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031113451"},{"type":"electronic","value":"9783031113468"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-11346-8_42","type":"book-chapter","created":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T03:33:48Z","timestamp":1658547228000},"page":"489-500","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MS-Net: A CNN Architecture for\u00a0Agriculture Pattern Segmentation in\u00a0Aerial Images"],"prefix":"10.1007","author":[{"given":"Sandesh","family":"Bhagat","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manesh","family":"Kokare","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vineet","family":"Haswani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Praful","family":"Hambarde","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravi","family":"Kamble","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,24]]},"reference":[{"issue":"12","key":"42_CR1","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."},{"key":"42_CR2","doi-asserted-by":"crossref","unstructured":"Bhagat, S., Kokare, M., Haswani, V., Hambarde, P., Kamble, R.: WheatNet-Lite: a novel light weight network for wheat head detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 1332\u20131341, October 2021","DOI":"10.1109\/ICCVW54120.2021.00154"},{"key":"42_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2022.101583","volume":"68","author":"S Bhagat","year":"2022","unstructured":"Bhagat, S., Kokare, M., Haswani, V., Hambarde, P., Kamble, R.: Eff-UNet++: a novel architecture for plant leaf segmentation and counting. Eco. Inform. 68, 101583 (2022)","journal-title":"Eco. Inform."},{"key":"42_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"42_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, B., et al.: SPGNet: semantic prediction guidance for scene parsing. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5218\u20135228 (2019)","DOI":"10.1109\/ICCV.2019.00532"},{"key":"42_CR6","doi-asserted-by":"crossref","unstructured":"Chiu, M.T., et al.: Agriculture-vision: a large aerial image database for agricultural pattern analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2828\u20132838 (2020)","DOI":"10.1109\/CVPR42600.2020.00290"},{"issue":"11","key":"42_CR7","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.3390\/s16111904","volume":"16","author":"P Christiansen","year":"2016","unstructured":"Christiansen, P., Nielsen, L.N., Steen, K.A., J\u00f8rgensen, R.N., Karstoft, H.: DeepAnomaly: combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors 16(11), 1904 (2016)","journal-title":"Sensors"},{"key":"42_CR8","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"42_CR9","doi-asserted-by":"crossref","unstructured":"Dudhane, A., Biradar, K.M., Patil, P.W., Hambarde, P., Murala, S.: Varicolored image de-hazing. In: proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4564\u20134573 (2020)","DOI":"10.1109\/CVPR42600.2020.00462"},{"issue":"2","key":"42_CR10","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vis."},{"issue":"7","key":"42_CR11","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","volume":"37","author":"H Fu","year":"2018","unstructured":"Fu, H., Cheng, J., Xu, Y., Wong, D.W.K., Liu, J., Cao, X.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans. Med. Imaging 37(7), 1597\u20131605 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"42_CR12","doi-asserted-by":"crossref","unstructured":"Hambarde, P., Dudhane, A., Murala, S.: Single image depth estimation using deep adversarial training. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 989\u2013993. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803027"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Hambarde, P., Dudhane, A., Patil, P.W., Murala, S., Dhall, A.: Depth estimation from single image and semantic prior. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 1441\u20131445. IEEE (2020)","DOI":"10.1109\/ICIP40778.2020.9190985"},{"key":"42_CR14","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1109\/TCI.2020.2981761","volume":"6","author":"P Hambarde","year":"2020","unstructured":"Hambarde, P., Murala, S.: S2DNet: depth estimation from single image and sparse samples. IEEE Trans. Computat. Imaging 6, 806\u2013817 (2020)","journal-title":"IEEE Trans. Computat. Imaging"},{"key":"42_CR15","doi-asserted-by":"crossref","unstructured":"Haug, S., Michaels, A., Biber, P., Ostermann, J.: Plant classification system for crop\/weed discrimination without segmentation. In: IEEE Winter Conference on Applications of Computer Vision, pp. 1142\u20131149. IEEE (2014)","DOI":"10.1109\/WACV.2014.6835733"},{"key":"42_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-319-16220-1_8","volume-title":"Computer Vision - ECCV 2014 Workshops","author":"S Haug","year":"2015","unstructured":"Haug, S., Ostermann, J.: A crop\/weed field image dataset for the evaluation of computer vision based precision agriculture tasks. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8928, pp. 105\u2013116. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16220-1_8"},{"key":"42_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"42_CR18","unstructured":"Howard, A., Zhmoginov, A., Chen, L.C., Sandler, M., Zhu, M.: Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation (2018)"},{"key":"42_CR19","doi-asserted-by":"crossref","unstructured":"Innani, S., Dutande, P., Baheti, B., Talbar, S., Baid, U.: Fuse-PN: a novel architecture for anomaly pattern segmentation in aerial agricultural images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2960\u20132968 (2021)","DOI":"10.1109\/CVPRW53098.2021.00331"},{"key":"42_CR20","doi-asserted-by":"crossref","unstructured":"Jin, S., et al.: Deep learning: individual maize segmentation from terrestrial lidar data using faster R-CNN and regional growth algorithms. Front. Plant Sci. 9, 866 (2018)","DOI":"10.3389\/fpls.2018.00866"},{"key":"42_CR21","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","volume":"147","author":"A Kamilaris","year":"2018","unstructured":"Kamilaris, A., Prenafeta-Bold\u00fa, F.X.: Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70\u201390 (2018)","journal-title":"Comput. Electron. Agric."},{"key":"42_CR22","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"42_CR23","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"42_CR24","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"},{"issue":"6","key":"42_CR25","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1002\/rob.21675","volume":"34","author":"P Lottes","year":"2017","unstructured":"Lottes, P., H\u00f6rferlin, M., Sander, S., Stachniss, C.: Effective vision-based classification for separating sugar beets and weeds for precision farming. J. Field Robot. 34(6), 1160\u20131178 (2017)","journal-title":"J. Field Robot."},{"key":"42_CR26","doi-asserted-by":"crossref","unstructured":"Lu, H., Fu, X., Liu, C., Li, L.G., He, Y.X., Li, N.W.: Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning. J. Mountain Sci. 14(4), 731\u2013741 (2017)","DOI":"10.1007\/s11629-016-3950-2"},{"key":"42_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105760","volume":"178","author":"Y Lu","year":"2020","unstructured":"Lu, Y., Young, S.: A survey of public datasets for computer vision tasks in precision agriculture. Comput. Electron. Agric. 178, 105760 (2020)","journal-title":"Comput. Electron. Agric."},{"issue":"4","key":"42_CR28","first-page":"1","volume":"6","author":"L Madhusudhan","year":"2015","unstructured":"Madhusudhan, L.: Agriculture role on Indian economy. Bus. Econ. J. 6(4), 1 (2015)","journal-title":"Bus. Econ. J."},{"issue":"3","key":"42_CR29","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1109\/LRA.2017.2667039","volume":"2","author":"C McCool","year":"2017","unstructured":"McCool, C., Perez, T., Upcroft, B.: Mixtures of lightweight deep convolutional neural networks: applied to agricultural robotics. IEEE Robot. Autom. Lett. 2(3), 1344\u20131351 (2017)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"42_CR30","doi-asserted-by":"crossref","unstructured":"Milioto, A., Lottes, P., Stachniss, C.: Real-time blob-wise sugar beets vs weeds classification for monitoring fields using convolutional neural networks. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 4 (2017)","DOI":"10.5194\/isprs-annals-IV-2-W3-41-2017"},{"key":"42_CR31","unstructured":"Mouret, F., Albughdadi, M., Duthoit, S., Kouam\u00e9, D., Rieu, G., Tourneret, J.Y.: Detecting anomalous crop development with multispectral and SAR time series using unsupervised outlier detection at the parcel-level: application to wheat and rapeseed crops (2020)"},{"key":"42_CR32","doi-asserted-by":"crossref","unstructured":"Olsen, A., et al.: DeepWeeds: a multiclass weed species image dataset for deep learning. Sci. Rep. 9(1), 1\u201312 (2019)","DOI":"10.1038\/s41598-018-38343-3"},{"key":"42_CR33","doi-asserted-by":"crossref","unstructured":"Patil, P.W., Biradar, K.M., Dudhane, A., Murala, S.: An end-to-end edge aggregation network for moving object segmentation. In: proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8149\u20138158 (2020)","DOI":"10.1109\/CVPR42600.2020.00817"},{"key":"42_CR34","doi-asserted-by":"publisher","first-page":"7889","DOI":"10.1109\/TIP.2021.3108405","volume":"30","author":"PW Patil","year":"2021","unstructured":"Patil, P.W., Dudhane, A., Kulkarni, A., Murala, S., Gonde, A.B., Gupta, S.: An unified recurrent video object segmentation framework for various surveillance environments. IEEE Trans. Image Process. 30, 7889\u20137902 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"42_CR35","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1109\/LSP.2021.3109774","volume":"28","author":"SS Phutke","year":"2021","unstructured":"Phutke, S.S., Murala, S.: Diverse receptive field based adversarial concurrent encoder network for image inpainting. IEEE Signal Process. Lett. 28, 1873\u20131877 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"42_CR36","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234\u2013241. Springer (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"42_CR37","doi-asserted-by":"crossref","unstructured":"Shah, J.P., Prajapati, H.B., Dabhi, V.K.: A survey on detection and classification of rice plant diseases. In: 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), pp. 1\u20138. IEEE (2016)","DOI":"10.1109\/ICCTAC.2016.7567333"},{"issue":"2","key":"42_CR38","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.tplants.2015.10.015","volume":"21","author":"A Singh","year":"2016","unstructured":"Singh, A., Ganapathysubramanian, B., Singh, A.K., Sarkar, S.: Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci. 21(2), 110\u2013124 (2016)","journal-title":"Trends Plant Sci."},{"key":"42_CR39","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"42_CR40","unstructured":"Chiu, M.T., et al.: The 1st agriculture-vision challenge: methods and results. arXiv e-prints pp. arXiv-2004 (2020)"}],"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\/978-3-031-11346-8_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T20:38:56Z","timestamp":1664311136000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-11346-8_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031113451","9783031113468"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-11346-8_42","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 July 2022","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":"Rupnagar","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":"3 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2021","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":"cvip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iitrpr.cvip2021.com\/","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":"260","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":"77","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":"20","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":"30% - 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)"}}]}}