{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:40:03Z","timestamp":1755862803484,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Foundation Ireland's Strategic Partnerships Programme","award":["16\/SPP\/3296"],"award-info":[{"award-number":["16\/SPP\/3296"]}]},{"name":"Origin Enterprises Plc"},{"name":"CONSUS"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,2,2]]},"DOI":"10.1145\/3651671.3651728","type":"proceedings-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T18:55:50Z","timestamp":1717786550000},"page":"153-160","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Winter Wheat Emergence and Stem Elongation Time using CNN"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9897-5294","authenticated-orcid":false,"given":"Yunan","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, University College Dublin, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3620-1395","authenticated-orcid":false,"given":"Sahraoui","family":"Dhelim","sequence":"additional","affiliation":[{"name":"School of Computer Science, University College Dublin, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0176-6281","authenticated-orcid":false,"given":"Mohand Tahar","family":"Kechadi","sequence":"additional","affiliation":[{"name":"School of Computer Science, University College Dublin, Ireland"}]}],"member":"320","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture10070305"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs14040893"},{"key":"e_1_3_2_1_3_1","volume-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","author":"Zhang Liangpei","year":"2016","unstructured":"[3] Liangpei Zhang, Lefei Zhang, and Bo\u00a0Du. Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and remote sensing magazine, 4(2):22\u201340, 2016."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0021859606006691"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2586602"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11172"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209811.3212707"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs11060619"},{"key":"e_1_3_2_1_9_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25","author":"Krizhevsky Alex","year":"2012","unstructured":"[9] Alex Krizhevsky, Ilya Sutskever, and Geoffrey\u00a0E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_11_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"[11] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"issue":"9","key":"e_1_3_2_1_13_1","first-page":"1300","article-title":"Convolutional neural networks in image understanding","volume":"42","author":"Chang Liang","year":"2016","unstructured":"[13] Liang Chang, Xiao-Ming Deng, Ming-Quan Zhou, Zhong-Ke Wu, Ye\u00a0Yuan, Shuo Yang, and Hong-An Wang. Convolutional neural networks in image understanding. Acta Automatica Sinica, 42(9):1300\u20131312, 2016.","journal-title":"Acta Automatica Sinica"},{"issue":"18","key":"e_1_3_2_1_14_1","first-page":"9","article-title":"Sparse coding of pathology slides compared to transfer learning with deep neural networks","volume":"19","author":"Fischer Will","year":"2018","unstructured":"[14] Will Fischer, Sanketh\u00a0S Moudgalya, Judith\u00a0D Cohn, Nga\u00a0TT Nguyen, and Garrett\u00a0T Kenyon. Sparse coding of pathology slides compared to transfer learning with deep neural networks. BMC bioinformatics, 19(18):9\u201317, 2018.","journal-title":"BMC bioinformatics"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05064-6"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403375"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs13214372"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2022.102828"},{"issue":"9","key":"e_1_3_2_1_20_1","first-page":"2022","article-title":"A systematic literature review on crop yield prediction with deep learning and remote sensing","volume":"14","author":"Muruganantham Priyanga","year":"1990","unstructured":"[20] Priyanga Muruganantham, Santoso Wibowo, Srimannarayana Grandhi, Nahidul\u00a0Hoque Samrat, and Nahina Islam. A systematic literature review on crop yield prediction with deep learning and remote sensing. Remote Sensing, 14(9):1990, 2022.","journal-title":"Remote Sensing"},{"key":"e_1_3_2_1_21_1","first-page":"5166","volume-title":"IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium","author":"Ghazaryan Gohar","unstructured":"[21] Gohar Ghazaryan, Sergii Skakun, Simon K\u00f6nig, Ehsan\u00a0Eyshi Rezaei, Stefan Siebert, and Olena Dubovyk. Crop yield estimation using multi-source satellite image series and deep learning. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, pages 5163\u20135166. IEEE, 2020."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-13232-y"},{"key":"e_1_3_2_1_23_1","first-page":"4","volume-title":"2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","author":"Mu Haowei","unstructured":"[23] Haowei Mu, Liang Zhou, Xuewei Dang, and Bo\u00a0Yuan. Winter wheat yield estimation from multitemporal remote sensing images based on convolutional neural networks. In 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), pages 1\u20134. IEEE, 2019."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs9050498"},{"key":"e_1_3_2_1_25_1","first-page":"241","volume-title":"International Conference on Medical image computing and computer-assisted intervention","author":"Ronneberger Olaf","unstructured":"[25] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234\u2013241. Springer, 2015."},{"key":"e_1_3_2_1_26_1","volume-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","author":"Chen Liang-Chieh","year":"2017","unstructured":"[26] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan\u00a0L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4):834\u2013848, 2017."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2016.60"},{"key":"e_1_3_2_1_28_1","first-page":"348","volume-title":"2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), volume\u00a02","author":"Qisong Xie Yi\u00a0Wan","unstructured":"[28] Yi\u00a0Wan and Qisong Xie. A novel framework for optimal rgb to grayscale image conversion. In 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), volume\u00a02, pages 345\u2013348. IEEE, 2016."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs14092022"}],"event":{"name":"ICMLC 2024: 2024 16th International Conference on Machine Learning and Computing","acronym":"ICMLC 2024","location":"Shenzhen China"},"container-title":["Proceedings of the 2024 16th International Conference on Machine Learning and Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3651671.3651728","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3651671.3651728","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:19:23Z","timestamp":1755861563000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3651671.3651728"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,2]]},"references-count":29,"alternative-id":["10.1145\/3651671.3651728","10.1145\/3651671"],"URL":"https:\/\/doi.org\/10.1145\/3651671.3651728","relation":{},"subject":[],"published":{"date-parts":[[2024,2,2]]},"assertion":[{"value":"2024-06-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}