{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T04:53:35Z","timestamp":1780808015132,"version":"3.54.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"DST, MST, Govt. of India.","award":["T-319"],"award-info":[{"award-number":["T-319"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s00521-023-08693-9","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T07:02:08Z","timestamp":1686121328000},"page":"18641-18664","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["PDS-MCNet: a hybrid framework using MobileNetV2 with SiLU6 activation function and capsule networks for disease severity estimation in plants"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7053-1654","authenticated-orcid":false,"given":"Shradha","family":"Verma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anuradha","family":"Chug","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit Prakash","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinesh","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"issue":"2","key":"8693_CR1","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.agsy.2010.11.003","volume":"104","author":"UA Schneider","year":"2011","unstructured":"Schneider UA, Havl\u00edk P, Schmid E, Valin H, Mosnier A, Obersteiner M, Fritz S (2011) Impacts of population growth, economic development, and technical change on global food production and consumption. Agric Syst 104(2):204\u2013215","journal-title":"Agric Syst"},{"key":"8693_CR2","doi-asserted-by":"crossref","unstructured":"Ahmad A, Saraswat D, & El Gamal A (2022) A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agric Technol 100083","DOI":"10.1016\/j.atech.2022.100083"},{"key":"8693_CR3","doi-asserted-by":"crossref","unstructured":"Verma S, Chug A, & Singh AP (2020) Recent advancements in image-based prediction models for diagnosis of plant diseases. In: Proceedings of 3rd international conference on computer vision and image processing (pp. 365\u2013377). Springer, Singapore","DOI":"10.1007\/978-981-32-9088-4_31"},{"key":"8693_CR4","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 FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70\u201390","journal-title":"Comput Electron Agric"},{"issue":"2","key":"8693_CR5","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 AK, Sarkar S (2016) Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21(2):110\u2013124","journal-title":"Trends Plant Sci"},{"key":"8693_CR6","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"SP Mohanty","year":"2016","unstructured":"Mohanty SP, Hughes DP, Salath\u00e9 M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419","journal-title":"Front Plant Sci"},{"key":"8693_CR7","first-page":"84","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:84\u201390","journal-title":"Adv Neural Inf Process Syst"},{"key":"8693_CR8","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":"8693_CR9","unstructured":"Hughes D, & Salath\u00e9 M (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060"},{"key":"8693_CR10","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","volume":"161","author":"EC Too","year":"2019","unstructured":"Too EC, Yujian L, Njuki S, Yingchun L (2019) A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 161:272\u2013279","journal-title":"Comput Electron Agric"},{"key":"8693_CR11","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"8693_CR12","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, & Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"8693_CR13","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":"8693_CR14","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, & Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"8693_CR15","doi-asserted-by":"crossref","unstructured":"Sladojevic S, Arsenovic M, Anderla A, Culibrk D, & Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci","DOI":"10.1155\/2016\/3289801"},{"key":"8693_CR16","doi-asserted-by":"crossref","unstructured":"Zhang K, Wu Q, Liu A, & Meng X (2018) Can deep learning identify tomato leaf disease?. Adv Multimed","DOI":"10.1155\/2018\/6710865"},{"key":"8693_CR17","doi-asserted-by":"crossref","unstructured":"Sun Y, Liu Y, Wang G, & Zhang H (2017) Deep learning for plant identification in natural environment. Comput Intell Neurosci","DOI":"10.1155\/2017\/7361042"},{"key":"8693_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2020.101182","volume":"61","author":"\u00dc Atila","year":"2021","unstructured":"Atila \u00dc, U\u00e7ar M, Akyol K, U\u00e7ar E (2021) Plant leaf disease classification using EfficientNet deep learning model. Eco Inform 61:101182","journal-title":"Eco Inform"},{"key":"8693_CR19","doi-asserted-by":"publisher","first-page":"378","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 convolutional neural networks. Neurocomputing 267:378\u2013384","journal-title":"Neurocomputing"},{"key":"8693_CR20","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","volume":"145","author":"KP Ferentinos","year":"2018","unstructured":"Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311\u2013318","journal-title":"Comput Electron Agric"},{"key":"8693_CR21","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.compeleceng.2019.04.011","volume":"76","author":"G Geetharamani","year":"2019","unstructured":"Geetharamani G, Pandian A (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electr Eng 76:323\u2013338","journal-title":"Comput Electr Eng"},{"key":"8693_CR22","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.compag.2018.04.002","volume":"161","author":"A Picon","year":"2019","unstructured":"Picon A, Alvarez-Gila A, Seitz M, Ortiz-Barredo A, Echazarra J, Johannes A (2019) Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agric 161:280\u2013290","journal-title":"Comput Electron Agric"},{"key":"8693_CR23","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1016\/j.procs.2018.07.070","volume":"133","author":"AK Rangarajan","year":"2018","unstructured":"Rangarajan AK, Purushothaman R, Ramesh A (2018) Tomato crop disease classification using pre-trained deep learning algorithm. Proc Comput Sci 133:1040\u20131047","journal-title":"Proc Comput Sci"},{"key":"8693_CR24","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.compbiomed.2016.09.008","volume":"78","author":"T Pal","year":"2016","unstructured":"Pal T, Jaiswal V, Chauhan RS (2016) DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants. Comput Biol Med 78:42\u201348","journal-title":"Comput Biol Med"},{"issue":"6","key":"8693_CR25","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1016\/j.molp.2017.04.009","volume":"10","author":"D Heckmann","year":"2017","unstructured":"Heckmann D, Schl\u00fcter U, Weber AP (2017) Machine learning techniques for predicting crop photosynthetic capacity from leaf reflectance spectra. Mol Plant 10(6):878\u2013890","journal-title":"Mol Plant"},{"issue":"4","key":"8693_CR26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0123262","volume":"10","author":"SEA Raza","year":"2015","unstructured":"Raza SEA, Prince G, Clarkson JP, Rajpoot NM (2015) Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS ONE 10(4):e0123262","journal-title":"PLoS ONE"},{"key":"8693_CR27","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.compag.2018.02.018","volume":"147","author":"Y Osroosh","year":"2018","unstructured":"Osroosh Y, Khot LR, Peters RT (2018) Economical thermal-RGB imaging system for monitoring agricultural crops. Comput Electron Agric 147:34\u201343","journal-title":"Comput Electron Agric"},{"issue":"1","key":"8693_CR28","first-page":"41","volume":"4","author":"V Singh","year":"2017","unstructured":"Singh V, Misra AK (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":"8693_CR29","doi-asserted-by":"crossref","unstructured":"Zhong Liu L, Zhang W, Bao Shu S, & Jin X (2013) Image recognition of wheat disease based on RBF support vector machine. In: 2013 international conference on advanced computer science and electronics information (ICACSEI 2013), pp. 307\u2013310. Atlantis Press","DOI":"10.2991\/icacsei.2013.77"},{"issue":"9","key":"8693_CR30","first-page":"622","volume":"14","author":"H Sabrol","year":"2016","unstructured":"Sabrol H, Kumar S (2016) Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. Int J Comput Sci Inf Secur 14(9):622","journal-title":"Int J Comput Sci Inf Secur"},{"issue":"1","key":"8693_CR31","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.compag.2007.01.015","volume":"57","author":"KY Huang","year":"2007","unstructured":"Huang KY (2007) Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Comput Electron Agric 57(1):3\u201311","journal-title":"Comput Electron Agric"},{"key":"8693_CR32","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.compag.2016.01.008","volume":"121","author":"CL Chung","year":"2016","unstructured":"Chung CL, Huang KJ, Chen SY, Lai MH, Chen YC, Kuo YF (2016) Detecting Bakanae disease in rice seedlings by machine vision. Comput Electron Agric 121:404\u2013411","journal-title":"Comput Electron Agric"},{"key":"8693_CR33","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.biosystemseng.2016.08.024","volume":"151","author":"M Dyrmann","year":"2016","unstructured":"Dyrmann M, Karstoft H, Midtiby HS (2016) Plant species classification using deep convolutional neural network. Biosys Eng 151:72\u201380","journal-title":"Biosys Eng"},{"key":"8693_CR34","doi-asserted-by":"crossref","unstructured":"Durmu\u015f H, G\u00fcne\u015f EO, & K\u0131rc\u0131 M (2017) Disease detection on the leaves of the tomato plants by using deep learning. In: 2017 6th international conference on agro-geoinformatics, pp. 1\u20135. IEEE","DOI":"10.1109\/Agro-Geoinformatics.2017.8047016"},{"issue":"2","key":"8693_CR35","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.compag.2010.06.001","volume":"73","author":"CB Singh","year":"2010","unstructured":"Singh CB, Jayas DS, Paliwal J, White ND (2010) Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital color imaging. Comput Electron Agric 73(2):118\u2013125","journal-title":"Comput Electron Agric"},{"key":"8693_CR36","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.compag.2017.09.012","volume":"142","author":"J Lu","year":"2017","unstructured":"Lu J, Hu J, Zhao G, Mei F, Zhang C (2017) An in-field automatic wheat disease diagnosis system. Comput Electron Agric 142:369\u2013379","journal-title":"Comput Electron Agric"},{"key":"8693_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101289","volume":"63","author":"V Tiwari","year":"2021","unstructured":"Tiwari V, Joshi RC, Dutta MK (2021) Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Eco Inform 63:101289","journal-title":"Eco Inform"},{"key":"8693_CR38","doi-asserted-by":"crossref","unstructured":"Mitra B, Chowdhury AR, Dey P, Hazra KK, Sinha AK, Hossain A & Meena RS (2021) Use of agrochemicals in agriculture: alarming issues and solutions. In: Input use efficiency for food and environmental security (pp. 85\u2013122). Springer, Singapore","DOI":"10.1007\/978-981-16-5199-1_4"},{"key":"8693_CR39","doi-asserted-by":"publisher","first-page":"117863022110430","DOI":"10.1177\/11786302211043033","volume":"15","author":"SM Demi","year":"2021","unstructured":"Demi SM, Sicchia SR (2021) Agrochemicals use practices and health challenges of smallholder farmers in Ghana. Environ Health Insights 15:11786302211043032","journal-title":"Environ Health Insights"},{"key":"8693_CR40","doi-asserted-by":"crossref","unstructured":"Mandal A, Sarkar B, Mandal S, Vithanage M, Patra AK, & Manna MC (2020) Impact of agrochemicals on soil health. In: Agrochemicals detection, treatment and remediation, pp. 161\u2013187. Butterworth-Heinemann","DOI":"10.1016\/B978-0-08-103017-2.00007-6"},{"issue":"1","key":"8693_CR41","first-page":"3","volume":"2","author":"A Majeed","year":"2018","unstructured":"Majeed A (2018) Application of agrochemicals in agriculture: benefits, risks and responsibility of stakeholders. J Food Sci Toxicol 2(1):3","journal-title":"J Food Sci Toxicol"},{"key":"8693_CR42","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s40858-021-00439-z","volume":"47","author":"CH Bock","year":"2021","unstructured":"Bock CH, Chiang KS, Del Ponte EM (2021) Plant disease severity estimated visually: a century of research, best practices, and opportunities for improving methods and practices to maximize accuracy. Trop Plant Pathol 47:25\u201342","journal-title":"Trop Plant Pathol"},{"key":"8693_CR43","doi-asserted-by":"crossref","unstructured":"Wang G, Sun Y, & Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosc","DOI":"10.1155\/2017\/2917536"},{"key":"8693_CR44","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1016\/j.compag.2019.01.034","volume":"157","author":"Q Liang","year":"2019","unstructured":"Liang Q, Xiang S, Hu Y, Coppola G, Zhang D, Sun W (2019) PD2SE-Net: computer-assisted plant disease diagnosis and severity estimation network. Comput Electron Agric 157:518\u2013529","journal-title":"Comput Electron Agric"},{"key":"8693_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105162","volume":"169","author":"JG Esgario","year":"2020","unstructured":"Esgario JG, Krohling RA, Ventura JA (2020) Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput Electron Agric 169:105162","journal-title":"Comput Electron Agric"},{"key":"8693_CR46","doi-asserted-by":"crossref","unstructured":"Fenu G, Malloci FM (2021) Using multioutput learning to diagnose plant disease and stress severity. Complexity","DOI":"10.1155\/2021\/6663442"},{"key":"8693_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105701","volume":"178","author":"P Wspanialy","year":"2020","unstructured":"Wspanialy P, Moussa M (2020) A detection and severity estimation system for generic diseases of tomato greenhouse plants. Comput Electron Agric 178:105701","journal-title":"Comput Electron Agric"},{"issue":"4","key":"8693_CR48","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1007\/s11119-020-09782-8","volume":"22","author":"G Hu","year":"2021","unstructured":"Hu G, Wei K, Zhang Y, Bao W, Liang D (2021) Estimation of tea leaf blight severity in natural scene images. Precision Agric 22(4):1239\u20131262","journal-title":"Precision Agric"},{"key":"8693_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106373","volume":"189","author":"C Wang","year":"2021","unstructured":"Wang C, Du P, Wu H, Li J, Zhao C, Zhu H (2021) A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net. Comput Electron Agric 189:106373","journal-title":"Comput Electron Agric"},{"issue":"7","key":"8693_CR50","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0272002","volume":"17","author":"D Palma","year":"2022","unstructured":"Palma D, Blanchini F, Montessoro PL (2022) A system-theoretic approach for image-based infectious plant disease severity estimation. PLoS ONE 17(7):e0272002","journal-title":"PLoS ONE"},{"key":"8693_CR51","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.791018","volume":"13","author":"J Abdulridha","year":"2022","unstructured":"Abdulridha J, Ampatzidis Y, Qureshi J, Roberts P (2022) Identification and classification of Downy Mildew severity stages in watermelon utilizing aerial and ground remote sensing and machine learning. Front Plant Sci 13:791018","journal-title":"Front Plant Sci"},{"key":"8693_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101492","volume":"67","author":"SA Pearline","year":"2022","unstructured":"Pearline SA, Kumar VS (2022) Performance analysis of real-time plant species recognition using bilateral network combined with machine learning classifier. Eco Inform 67:101492","journal-title":"Eco Inform"},{"key":"8693_CR53","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, & Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"8693_CR54","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, & Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications.\u00a0arXiv preprint arXiv:1704.04861"},{"key":"8693_CR55","doi-asserted-by":"crossref","unstructured":"Wang Z, Wang W, Yang Y, Han Z, Xu D, & Su C (2022) CNN\u2010and GAN\u2010based classification of malicious code families: a code visualization approach. Int J Intell Syst","DOI":"10.1002\/int.23094"},{"key":"8693_CR56","unstructured":"Sabour S, Frosst N, & Hinton GE (2017) Dynamic routing between capsules. Adv Neural Inf Process Syst 30"},{"key":"8693_CR57","unstructured":"Ramachandran P, Zoph B, & Le QV (2017) Swish: a self-gated activation function. arXiv preprint arXiv:1710.05941, 7(1): 5"},{"issue":"10","key":"8693_CR58","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1109\/TIP.2008.2002826","volume":"17","author":"J Mukherjee","year":"2008","unstructured":"Mukherjee J, Mitra SK (2008) Enhancement of color images by scaling the DCT coefficients. IEEE Trans Image Process 17(10):1783\u20131794","journal-title":"IEEE Trans Image Process"},{"key":"8693_CR59","unstructured":"https:\/\/github.com\/XifengGuo\/CapsNet-Keras"},{"issue":"6","key":"8693_CR60","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.3390\/s22062285","volume":"22","author":"JGA Barbedo","year":"2022","unstructured":"Barbedo JGA (2022) Data fusion in agriculture: resolving ambiguities and closing data gaps. Sensors 22(6):2285","journal-title":"Sensors"},{"key":"8693_CR61","volume-title":"Digital image processing","author":"RC Gonzalez","year":"2005","unstructured":"Gonzalez RC, Woods RE (2005) Digital image processing. Pearson Education, London"},{"issue":"1","key":"8693_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42483-020-00049-8","volume":"2","author":"CH Bock","year":"2020","unstructured":"Bock CH, Barbedo JG, Del Ponte EM, Bohnenkamp D, Mahlein AK (2020) From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. Phytopathol Res 2(1):1\u201330","journal-title":"Phytopathol Res"},{"issue":"3","key":"8693_CR63","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-1550-6","volume":"17","author":"Y Yang","year":"2023","unstructured":"Yang Y, Wei X, Xu R, Wang W, Peng L, Wang Y (2023) Jointly beam stealing attackers detection and localization without training: an image processing viewpoint. Front Comp Sci 17(3):173704","journal-title":"Front Comp Sci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08693-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08693-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08693-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T08:16:38Z","timestamp":1692692198000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08693-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,7]]},"references-count":63,"journal-issue":{"issue":"25","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["8693"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08693-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,7]]},"assertion":[{"value":"25 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2023","order":3,"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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}