{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:27:40Z","timestamp":1771064860478,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T00:00:00Z","timestamp":1694822400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T00:00:00Z","timestamp":1694822400000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16680-4","type":"journal-article","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T11:01:38Z","timestamp":1694862098000},"page":"31379-31394","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["MAM-IncNet: an end-to-end deep learning detector for Camellia pest recognition"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1748-4374","authenticated-orcid":false,"given":"Junde","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weirong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Y. A.","family":"Nanehkaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. D.","family":"Suzauddola","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,16]]},"reference":[{"issue":"2","key":"16680_CR1","first-page":"189","volume":"4","author":"F Fina","year":"2013","unstructured":"Fina F, Birch P, Young R, Obu J, Faithpraise B, Chatwin C (2013) Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters. Int J Adv Biotechnol Res 4(2):189\u2013199","journal-title":"Int J Adv Biotechnol Res"},{"key":"16680_CR2","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.asoc.2014.11.046","volume":"28","author":"Y Kaya","year":"2015","unstructured":"Kaya Y, Kayci L, Uyar M (2015) Automatic identification of butterfly species based on local binary patterns and artificial neural network. Appl Soft Comput 28:132\u2013137","journal-title":"Appl Soft Comput"},{"key":"16680_CR3","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.knosys.2012.03.014","volume":"33","author":"J Wang","year":"2012","unstructured":"Wang J, Lin C, Ji L, Liang A (2012) A new automatic identification system of insect images at the order level. Knowl-Based Syst 33:102\u2013110","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"16680_CR4","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.aspen.2013.12.004","volume":"17","author":"SH Kang","year":"2014","unstructured":"Kang SH, Cho JH, Lee SH (2014) Identification of butterfly based on their shapes when viewed from different angles using an artificial neural network. J Asia-Pac Entomol 17(2):143\u2013149","journal-title":"J Asia-Pac Entomol"},{"key":"16680_CR5","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.compag.2017.03.016","volume":"137","author":"MA Ebrahimi","year":"2017","unstructured":"Ebrahimi MA, Khoshtaghaza MH, Minaei S, Jamshidi B (2017) Vision-based pest detection based on SVM classification method. Comput Electron Agric 137:52\u201358","journal-title":"Comput Electron Agric"},{"issue":"15","key":"16680_CR6","doi-asserted-by":"publisher","first-page":"20797","DOI":"10.1007\/s11042-022-12620-w","volume":"81","author":"W Chen","year":"2022","unstructured":"Chen W, Chen J, Zeb A, Yang S, Zhang D (2022) Mobile convolution neural network for the recognition of potato leaf disease images. Multimed Tools Appl 81(15):20797\u201320816","journal-title":"Multimed Tools Appl"},{"key":"16680_CR7","doi-asserted-by":"crossref","unstructured":"Hssayni EH, Joudar NE, Ettaouil M (2022) Localization and reduction of redundancy in CNN using L 1-sparsity induction.\u00a0J Ambient Intell Humaniz Comput\u00a01\u201313","DOI":"10.1007\/s12652-022-04025-2"},{"key":"16680_CR8","doi-asserted-by":"crossref","unstructured":"Ning X et al (2023) Hyper-sausage coverage function neuron model and learning algorithm for image classification. Pattern Recognit 136:109216","DOI":"10.1016\/j.patcog.2022.109216"},{"key":"16680_CR9","doi-asserted-by":"crossref","unstructured":"Tian S et al (2023) Continuous transfer of neural network representational similarity for incremental learning. Neurocomputing 545:126300","DOI":"10.1016\/j.neucom.2023.126300"},{"key":"16680_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109567","volume":"253","author":"N-E Joudar","year":"2022","unstructured":"Joudar N-E, Ettaouil M (2022) An adaptive Drop method for deep neural networks regularization: estimation of DropConnect hyperparameter using generalization gap. Knowl-Based Syst 253:109567","journal-title":"Knowl-Based Syst"},{"key":"16680_CR11","doi-asserted-by":"crossref","unstructured":"Hssayni El H, Joudar N\u2010E, Ettaouil M (2022) A deep learning framework for time series classification using normal cloud representation and convolutional neural network optimization. Comput Intell 38(6):2056\u20132074","DOI":"10.1111\/coin.12556"},{"key":"16680_CR12","doi-asserted-by":"crossref","unstructured":"Ning X et al (2022) HCFNN: high-order coverage function neural network for image classification. Pattern Recognit 131:108873","DOI":"10.1016\/j.patcog.2022.108873"},{"key":"16680_CR13","doi-asserted-by":"crossref","unstructured":"Shijie J, Peiyi J, Siping H (2017) Automatic detection of tomato diseases and pests based on leaf images. In: 2017 Chinese automation congress (CAC), IEEE, pp 2537\u20132510","DOI":"10.1109\/CAC.2017.8243388"},{"issue":"1","key":"16680_CR14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-016-0001-8","volume":"6","author":"Z Liu","year":"2016","unstructured":"Liu Z, Gao J, Yang G, Zhang H, He Y (2016) Localization and classification of paddy field pests using a saliency map and deep convolutional neural network. Sci Rep 6(1):1\u201312","journal-title":"Sci Rep"},{"key":"16680_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.104906","volume":"164","author":"K Thenmozhi","year":"2019","unstructured":"Thenmozhi K, Reddy US (2019) Crop pest classification based on deep convolutional neural network and transfer learning. Comput Electron Agric 164:104906","journal-title":"Comput Electron Agric"},{"issue":"9","key":"16680_CR16","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.3390\/s17092022","volume":"17","author":"A Fuentes","year":"2017","unstructured":"Fuentes A, Yoon S, Kim SC, Park DS (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9):2022","journal-title":"Sensors"},{"issue":"9","key":"16680_CR17","doi-asserted-by":"publisher","first-page":"1731","DOI":"10.1111\/ppa.13251","volume":"69","author":"SH Lee","year":"2020","unstructured":"Lee SH, Lin SR, Chen SF (2020) Identification of tea foliar diseases and pest damage under practical field conditions using a convolutional neural network. Plant Pathol 69(9):1731\u20131739","journal-title":"Plant Pathol"},{"key":"16680_CR18","first-page":"533","volume":"12","author":"X Wang","year":"2021","unstructured":"Wang X, Liu J (2021) Tomato anomalies detection in greenhouse scenarios based on YOLO-Dense. Front Plant Sci 12:533","journal-title":"Front Plant Sci"},{"key":"16680_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, ... Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"16680_CR20","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv: Computer Vision and Pattern Recognition"},{"key":"16680_CR21","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"16680_CR22","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence (vol. 31, no. 1)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"16680_CR23","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13713\u201313722","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"16680_CR24","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262015417.001.0001","volume-title":"A computational perspective on visual attention","author":"JK Tsotsos","year":"2011","unstructured":"Tsotsos JK (2011) A computational perspective on visual attention. MIT Press"},{"key":"16680_CR25","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 603\u2013612","DOI":"10.1109\/ICCV.2019.00069"},{"key":"16680_CR26","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"16680_CR27","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhang L, Cheng MM, Feng J (2020) Strip pooling: Rethinking spatial pooling for scene parsing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4003\u20134012","DOI":"10.1109\/CVPR42600.2020.00406"},{"key":"16680_CR28","unstructured":"Hu J, Shen L, Albanie S, Sun G, Vedaldi A (2018)\u00a0Gather-excite: Exploiting feature context\u00a0in convolutional neural networks. Advances in neural information processing systems, 31"},{"key":"16680_CR29","doi-asserted-by":"crossref","unstructured":"Bello I, Zoph B, Vaswani A, Shlens J, Le QV (2019) Attention augmented convolutional networks. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3286\u20133295","DOI":"10.1109\/ICCV.2019.00338"},{"key":"16680_CR30","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"16680_CR31","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"16680_CR32","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision. Springer, Cham, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"16680_CR33","doi-asserted-by":"crossref","unstructured":"Zhao Z, Xu G, Qi Y, Liu N, Zhang T (2016)\u00a0Multi-patch deep features for power line insulator\u00a0status classification from aerial images. In: 2016 international joint conference on neural networks (IJCNN), IEEE, pp 3187\u20133194","DOI":"10.1109\/IJCNN.2016.7727606"},{"key":"16680_CR34","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition.\u00a0arXiv preprint arXiv:1409.1556"},{"key":"16680_CR35","doi-asserted-by":"crossref","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, MaS ... Fei-Fei L (2015)\u00a0Imagenet large\u00a0scale visual recognition challenge.\u00a0Int J Comput Vision 115(3):211\u2013252","DOI":"10.1007\/s11263-015-0816-y"},{"key":"16680_CR36","unstructured":"Kingma DP, Ba JL (2015) Adam: A Method for Stochastic Optimization. In: ICLR 2015\u202f: International conference on learning representations 2015"},{"key":"16680_CR37","doi-asserted-by":"crossref","unstructured":"Younis A, Shixin L, Jn S, Hai Z (2020)\u00a0Real-time object detection using pre-trained deep\u00a0learning models MobileNet-SSD. In: Proceedings of 2020 the 6th international conference on computing and data\u00a0engineering, pp 44\u201348","DOI":"10.1145\/3379247.3379264"},{"key":"16680_CR38","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"16680_CR39","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:91\u201399","journal-title":"Adv Neural Inf Process Syst"},{"key":"16680_CR40","doi-asserted-by":"crossref","unstructured":"Liu S, Huang D (2018) Receptive field block net for accurate and fast object detection. In: Proceedings of the European conference on computer vision (ECCV), pp 385\u2013400","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"16680_CR41","doi-asserted-by":"crossref","unstructured":"Zhao Q, Sheng T, Wang Y, Tang Z, Chen Y, Cai L, Ling H (2019) M2det: A single-shot object detector based on multi-level feature pyramid network. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, no 01, pp 9259\u20139266","DOI":"10.1609\/aaai.v33i01.33019259"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16680-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16680-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16680-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T06:39:59Z","timestamp":1709879999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16680-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,16]]},"references-count":41,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16680"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16680-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,16]]},"assertion":[{"value":"5 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}