{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T08:27:22Z","timestamp":1777624042986,"version":"3.51.4"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T00:00:00Z","timestamp":1643328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T00:00:00Z","timestamp":1643328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001845","name":"Science for Equity, Empowerment and Development Division","doi-asserted-by":"publisher","award":["SP\/YO\/688\/2018"],"award-info":[{"award-number":["SP\/YO\/688\/2018"]}],"id":[{"id":"10.13039\/501100001845","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11042-021-11866-0","type":"journal-article","created":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T19:02:16Z","timestamp":1643396536000},"page":"22323-22334","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["LWCNN: a lightweight convolutional neural network for agricultural crop protection"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4975-4121","authenticated-orcid":false,"given":"Sundaresan","family":"Raman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manan","family":"Soni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohit","family":"Ramaprasad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinay","family":"Chamola","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,28]]},"reference":[{"key":"11866_CR1","doi-asserted-by":"crossref","unstructured":"Akhtar A, Khanum A, Khan SA, Shaukat A (2013) Automated plant disease analysis (apda): Performance comparison of machine learning techniques. In: 11th International Conference on Frontiers of Information Technology, 2013, pp 60\u201365","DOI":"10.1109\/FIT.2013.19"},{"key":"11866_CR2","unstructured":"Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. In: BTW, pp 79-88"},{"key":"11866_CR3","doi-asserted-by":"publisher","unstructured":"Boulent J, Foucher S, Th\u00b4eau J, St-Charles P-L (2019) Convolutional neural networks for the automatic identification of plant diseases. Front Plant Sci 10:941, [Online]. Available: https:\/\/www.frontiersin.org\/article\/https:\/\/doi.org\/10.3389\/fpls.2019.00941","DOI":"10.3389\/fpls.2019.00941"},{"key":"11866_CR4","doi-asserted-by":"crossref","unstructured":"Cheng X, Zhang Y, Chen Y, Wu Y, Yue Y (2017) Pest identification via deep residual learning in complex background. Comput Electron Agric 141:351\u2013356","DOI":"10.1016\/j.compag.2017.08.005"},{"key":"11866_CR5","doi-asserted-by":"crossref","unstructured":"Coulibaly S, Kamsu-Foguem B, Kamissoko D, Traore D (2019) Deep neural networks with transfer learning in millet crop images. Comput Ind 108:115\u2013120, [Online]. Available: https:\/\/hal.archives-ouvertes.fr\/hal-02104287","DOI":"10.1016\/j.compind.2019.02.003"},{"key":"11866_CR6","doi-asserted-by":"crossref","unstructured":"Es-saady Y, El Massi I, El Yassa M, Mammass D, Benazoun A (2016) Automatic recognition of plant leaves diseases based on serial combination of two svm classifiers. In: International Conference on Electrical and Information Technologies (ICEIT), 2016, pp 561\u2013566","DOI":"10.1109\/EITech.2016.7519661"},{"key":"11866_CR7","doi-asserted-by":"crossref","unstructured":"Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311\u2013318, [Online]. Available: http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0168169917311742","DOI":"10.1016\/j.compag.2018.01.009"},{"issue":"080","key":"11866_CR8","first-page":"069","volume":"7","author":"P Jiang","year":"2019","unstructured":"Jiang P, Chen Y, Liu B, He D, Liang C (2019)Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 7(080):069\u2013059","journal-title":"IEEE Access"},{"key":"11866_CR9","doi-asserted-by":"crossref","unstructured":"Kale AP, Sonavane SP (2019) Iot based smart farming: Feature subset selection for optimized high-dimensional data using improved ga based approach for elm. Computers and Electronics in Agriculture 161:225\u2013232. bigData and DSS in Agriculture","DOI":"10.1016\/j.compag.2018.04.027"},{"key":"11866_CR10","doi-asserted-by":"publisher","unstructured":"Liakos K, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: A review. Sensors 18(8):2674, 08 [Online]. Available: https:\/\/doi.org\/10.3390\/s18082674","DOI":"10.3390\/s18082674"},{"key":"11866_CR11","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00b4ar P (2017) Focal loss for dense object detection. IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp 2999-3007","DOI":"10.1109\/ICCV.2017.324"},{"key":"11866_CR12","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector. In: Computer Vision \u2013 ECCV 2016. Leibe B, Matas J, Sebe N, Welling M (ed). Springer International Publishing, Cham, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"11866_CR13","doi-asserted-by":"publisher","first-page":"07","DOI":"10.1016\/j.neucom.2017.06.023","volume":"267","author":"Y Lu","year":"2017","unstructured":"Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolu- tional neural networks. Neurocomputing 267:07","journal-title":"Neurocomputing"},{"issue":"538","key":"11866_CR14","first-page":"529","volume":"7","author":"Q Mao","year":"2019","unstructured":"Mao Q, Sun H, Liu Y, Jia R (2019) Mini-yolov3: Real-time object detector for embedded applications. IEEE Access 7(538):529\u2013133","journal-title":"IEEE Access"},{"key":"11866_CR15","doi-asserted-by":"publisher","unstructured":"Mohanty SP, Hughes DP, Salath\u00b4e M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419, [Online]. Available: https:\/\/www.frontiersin.org\/article\/https:\/\/doi.org\/10.3389\/fpls.2016.01419","DOI":"10.3389\/fpls.2016.01419"},{"key":"11866_CR16","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1587\/transinf.2016EDL8180","volume":"100\u2013D","author":"X Ning","year":"2017","unstructured":"Ning X, Li W, Liu W (2017) A fast single image haze removal method based on human retina property. IEICE Trans Inf Syst 100\u2013D:211\u2013214","journal-title":"IEICE Trans Inf Syst"},{"key":"11866_CR17","doi-asserted-by":"crossref","unstructured":"Ning X, Duan P, Li W, Zhang S (2020)Real-time 3d face alignment using an encoder-decoder network with an efficient deconvolution layer. IEEE Signal Process Lett 27:1944\u20131948","DOI":"10.1109\/LSP.2020.3032277"},{"key":"11866_CR18","doi-asserted-by":"publisher","unstructured":"Ramcharan A, Baranowski K, McCloskey P, Ahmed B, Legg J, Hughes DP (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8:1852, [Online]. Available: https:\/\/www.frontiersin.org\/article\/https:\/\/doi.org\/10.3389\/fpls.2017.01852","DOI":"10.3389\/fpls.2017.01852"},{"key":"11866_CR19","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement. arXiv:1804.02767 [cs], Apr. arXiv: 1804.02767. [Online]. Available: http:\/\/arxiv.org\/abs\/1804.02767"},{"key":"11866_CR20","doi-asserted-by":"publisher","unstructured":"Saleem P, Arif M (2019) Plant disease detection and classification by deep learning. Plants 8(11):468, 10 [Online]. Available: https:\/\/doi.org\/10.3390\/plants8110468","DOI":"10.3390\/plants8110468"},{"key":"11866_CR21","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard AG, Zhu M, Zhmoginov A, Chen L (2018) \u201cMobilenetv2: Inverted residuals and linear bottlenecks,\u201d in 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"11866_CR22","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2020) Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Int J Comput Vis 128(2):336\u2013359, Feb. arXiv: 1610.02391. [Online]. Available: http:\/\/arxiv.org\/abs\/1610.02391","DOI":"10.1007\/s11263-019-01228-7"},{"issue":"1","key":"11866_CR23","first-page":"41","volume":"4","author":"V Singh","year":"2017","unstructured":"Singh V, Misra A (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 4(1):41\u201349","journal-title":"Inf Process Agric"},{"key":"11866_CR24","unstructured":"Singh SD, King SB, Werder J (1993). Downy mildew disease of pearl millet. Infor\u00admation Bulletin no. 37. (In En. Summaries in Fr, Es.) Patancheru, A.P. 502 324, India: International Crops Research Institute for the Semi-Arid Tropics, pp 36"},{"key":"11866_CR25","unstructured":"Tan M, Le QV (2019) Efficientnet: Rethinking model scaling for convolutional neural networks, International Conference on Machine Learning, pp. 6105-6114"},{"key":"11866_CR26","doi-asserted-by":"crossref","unstructured":"Wang H, Li G, Ma Z, Li X (2012) Image recognition of plant diseases based on principal component analysis and neural networks. In: 8th International Conference on Natural Computation, pp. 246\u2013251","DOI":"10.1109\/ICNC.2012.6234701"},{"key":"11866_CR27","doi-asserted-by":"crossref","unstructured":"Wolfert S, Ge L, Verdouw C, Bogaardt M (2017) Big data in smart farming \u2013 a review. Agric Syst 153:69\u201380","DOI":"10.1016\/j.agsy.2017.01.023"},{"key":"11866_CR28","doi-asserted-by":"publisher","first-page":"8834","DOI":"10.1109\/ACCESS.2020.2964838","volume":"8","author":"N Xin","year":"2020","unstructured":"Xin N, Pengfei D, Weijun L, Yuan S, Shuang L (2020) A cpu real-time face alignment for mobile platform. IEEE Access 8:8834\u20138843","journal-title":"IEEE Access"},{"key":"11866_CR29","doi-asserted-by":"publisher","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2018) \"Learning Transferable Architectures for Scalable Image Recognition,\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8697-8710. https:\/\/doi.org\/10.1109\/CVPR.2018.00907","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11866-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11866-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11866-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T07:34:53Z","timestamp":1655883293000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11866-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,28]]},"references-count":29,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["11866"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11866-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,28]]},"assertion":[{"value":"20 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}