{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T18:16:44Z","timestamp":1787509004314,"version":"build-2736575974"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00521-023-09187-4","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T14:02:24Z","timestamp":1700056944000},"page":"1773-1789","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Impact of datasets on the effectiveness of MobileNet for beans leaf disease detection"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6171-5531","authenticated-orcid":false,"given":"Elhoucine","family":"Elfatimi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Recep","family":"Eryi\u011fit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harisu Abdullahi","family":"Shehu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"9187_CR1","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.agsy.2019.04.002","volume":"173","author":"E Loizou","year":"2019","unstructured":"Loizou E, Karelakis C, Galanopoulos K, Mattas K (2019) The role of agriculture as a development tool for a regional economy. Agric Syst 173:482\u2013490","journal-title":"Agric Syst"},{"issue":"3","key":"9187_CR2","first-page":"1","volume":"6","author":"L Praburaj","year":"2018","unstructured":"Praburaj L, Design F, Nadu T (2018) Role of agriculture in the economic development of a country. Shanlax Int J Commer 6(3):1\u20135","journal-title":"Shanlax Int J Commer"},{"key":"9187_CR3","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1094\/PHYTO.2001.91.11.1045","volume":"91","author":"W De Jesus","year":"2001","unstructured":"De Jesus W, Do Vale F, Coelho R, Hau B, Zambolim L, Costa L, Filho AB (2001) Effects of angular leaf spot and rust on yield loss of Phaseolus vulgaris. Phytopathology 91:1045\u20131053","journal-title":"Phytopathology"},{"issue":"2","key":"9187_CR4","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s41315-021-00174-3","volume":"5","author":"SH Abed","year":"2021","unstructured":"Abed SH, Al-Waisy AS, Mohammed HJ, Al-Fahdawi S (2021) A modern deep learning framework in robot vision for automated bean leaves diseases detection. Int J Intell Robot Appl 5(2):235\u2013251","journal-title":"Int J Intell Robot Appl"},{"issue":"1","key":"9187_CR5","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":"9187_CR6","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"9187_CR7","unstructured":"Makere AL (2020) \u201cibean\u201d, National Crops Resources Research Institute (NaCRRI)"},{"issue":"1","key":"9187_CR8","first-page":"796","volume":"10","author":"P Sahu","year":"2021","unstructured":"Sahu P, Chug A, Singh AP, Singh D, Singh RP (2021) Deep learning models for beans crop diseases: classification and visualization techniques. Int J Mod Agric 10(1):796\u2013812","journal-title":"Int J Mod Agric"},{"issue":"3","key":"9187_CR9","doi-asserted-by":"publisher","first-page":"343","DOI":"10.3390\/sym11030343","volume":"11","author":"J Chen","year":"2019","unstructured":"Chen J, Liu Q, Gao L (2019) Visual tea leaf disease recognition using a convolutional neural network model. Symmetry 11(3):343","journal-title":"Symmetry"},{"key":"9187_CR10","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.compag.2018.08.048","volume":"154","author":"J Ma","year":"2018","unstructured":"Ma J, Du K, Zheng F, Zhang L, Gong Z, Sun Z (2018) A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric 154:18\u201324","journal-title":"Comput Electron Agric"},{"key":"9187_CR11","first-page":"114010A","volume-title":"Real-time image processing and deep learning","author":"B Richey","year":"2020","unstructured":"Richey B, Majumder S, Shirvaikar M, Kehtarnavaz N (2020) Real-time detection of maize crop disease via a deep learning-based smartphone app. Real-time image processing and deep learning. International Society for Optics and Photonics, p 114010A"},{"key":"9187_CR12","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"},{"issue":"2","key":"9187_CR13","doi-asserted-by":"publisher","first-page":"294","DOI":"10.3390\/agriengineering3020020","volume":"3","author":"ME Chowdhury","year":"2021","unstructured":"Chowdhury ME, Rahman T, Khandakar A, Ayari MA, Khan AU, Khan MS, Al-Emadi N, Reaz MBI, Islam MT, Ali SHM (2021) Automatic and reliable leaf disease detection using deep learning techniques. AgriEngineering 3(2):294\u2013312","journal-title":"AgriEngineering"},{"issue":"4","key":"9187_CR14","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1080\/08839514.2017.1315516","volume":"31","author":"M Brahimi","year":"2017","unstructured":"Brahimi M, Boukhalfa K, Moussaoui A (2017) Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell 31(4):299\u2013315","journal-title":"Appl Artif Intell"},{"issue":"2","key":"9187_CR15","first-page":"290","volume":"9","author":"SZM Zaki","year":"2020","unstructured":"Zaki SZM, Zulkifley MA, Stofa MM, Kamari NAM, Mohamed NA (2020) Classification of tomato leaf diseases using MobileNet v2. IAES Int J Artif Intell 9(2):290","journal-title":"IAES Int J Artif Intell"},{"issue":"1","key":"9187_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-020-00624-2","volume":"16","author":"J Liu","year":"2020","unstructured":"Liu J, Wang X (2020) Early recognition of tomato gray leaf spot disease based on the mobilenetv2-yolov3 model. Plant Methods 16(1):1\u201316","journal-title":"Plant Methods"},{"key":"9187_CR17","doi-asserted-by":"publisher","first-page":"012009","DOI":"10.1088\/1742-6596\/1845\/1\/012009","volume":"1845","author":"A Sembiring","year":"2021","unstructured":"Sembiring A, Away Y, Arnia F, Muharar R (2021) Development of concise convolutional neural network for tomato plant disease classification based on leaf images. J Phys Conf Ser 1845:012009","journal-title":"J Phys Conf Ser"},{"key":"9187_CR18","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.compag.2018.08.013","volume":"153","author":"JGA Barbedo","year":"2018","unstructured":"Barbedo JGA (2018) Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Comput Electron Agric 153:46\u201353","journal-title":"Comput Electron Agric"},{"key":"9187_CR19","unstructured":"Vinutha M, Kharbanda R, Rashmi B, Rajani S, Pareek R (2019) Crop monitoring: using mobilenet models. Int Res J Eng Technol IRJET 6(05)"},{"key":"9187_CR20","doi-asserted-by":"publisher","first-page":"108796","DOI":"10.1016\/j.measurement.2020.108796","volume":"171","author":"SS Chouhan","year":"2021","unstructured":"Chouhan SS, Singh UP, Sharma U, Jain S (2021) Leaf disease segmentation and classification of Jatropha curcas L. and Pongamia pinnatal L. biofuel plants using computer vision based approaches. Measurement 171:108796","journal-title":"Measurement"},{"key":"9187_CR21","doi-asserted-by":"crossref","unstructured":"Arya S, Singh R (2019) A comparative study of CNN and AlexNet for detection of disease in potato and mango leaf. In: 2019 international conference on issues and challenges in intelligent computing techniques (ICICT), vol 1. IEEE, pp 1\u20136","DOI":"10.1109\/ICICT46931.2019.8977648"},{"key":"9187_CR22","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.procs.2020.03.225","volume":"167","author":"M Agarwal","year":"2020","unstructured":"Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S (2020) Toled: tomato leaf disease detection using convolution neural network. Procedia Comput Sci 167:293\u2013301","journal-title":"Procedia Comput Sci"},{"key":"9187_CR23","first-page":"721","volume":"7","author":"UP Singh","year":"2021","unstructured":"Singh UP, Chouhan SS, Jain S, Jain S (2021) Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access 7:721\u2013729","journal-title":"IEEE Access"},{"key":"9187_CR24","doi-asserted-by":"crossref","unstructured":"Kaur M, Bhatia R (2019) Development of an improved tomato leaf disease detection and classification method. In: Proceedings of the 2022 IEEE conference on information and communication technology, Baghdad, Iraq, pp 1\u20135","DOI":"10.1109\/CICT48419.2019.9066230"},{"key":"9187_CR25","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 17:2022","journal-title":"Sensors"},{"key":"9187_CR26","doi-asserted-by":"crossref","unstructured":"Aldhyani TH, Alkahtani H, Eunice RJ, Hemanth DJ (2022) Leaf pathology detection in potato and pepper bell plant using convolutional neural networks. In: Proceedings of the 2022 7th international conference on communication and electronics systems (ICCES), Coimbatore, India, 22\u201324, pp 1289\u20131294","DOI":"10.1109\/ICCES54183.2022.9835735"},{"issue":"2","key":"9187_CR27","first-page":"1154","volume":"12","author":"M Bhanusri","year":"2020","unstructured":"Bhanusri M, Ramesh N, Razia S (2020) Maize leaf disease detection using convolution neural network. J Adv Res Dyn Control Syst 12(2):1154\u20131160","journal-title":"J Adv Res Dyn Control Syst"},{"issue":"1","key":"9187_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-018-0366-8","volume":"14","author":"MM Hasan","year":"2018","unstructured":"Hasan MM, Chopin JP, Laga H, Miklavcic SJ (2018) Detection and analysis of wheat spikes using convolutional neural networks. Plant Methods 14(1):1\u201313","journal-title":"Plant Methods"},{"key":"9187_CR29","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1007\/s00371-021-02164-9","volume":"200","author":"R Gajjar","year":"2021","unstructured":"Gajjar R, Gajjar N, Thakor VJ, Patel NP, Ruparelia S (2021) Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform. Vis Comput 200:300. https:\/\/doi.org\/10.1007\/s00371-021-02164-9","journal-title":"Vis Comput"},{"key":"9187_CR30","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.matpr.2021.05.584","volume":"51","author":"S Ashwinkumar","year":"2022","unstructured":"Ashwinkumar S, Rajagopal S, Manimaran V, Jegajothi B (2022) Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks. Mater Today Proc 51:480\u2013487","journal-title":"Mater Today Proc"},{"key":"9187_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3142817","author":"E Elfatimi","year":"2022","unstructured":"Elfatimi E, Eryi\u011fit R, Elfatimi L (2022) Beans leaf disease classification using MobiletNet models. IEEE Open Access. https:\/\/doi.org\/10.1109\/ACCESS.2022.3142817","journal-title":"IEEE Open Access"},{"key":"9187_CR32","first-page":"235","volume":"5","author":"HA Sudad","year":"2022","unstructured":"Sudad HA, Alaa SA, Hussam JM, Shumoos A (2022) A modern deep learning framework in robot vision for automated bean leaves diseases detection. Int J Intell Robot Appl 5:235\u2013251","journal-title":"Int J Intell Robot Appl"},{"key":"9187_CR33","unstructured":"Shehu HA, Rabie A, Sharif MH et al (2021) Artificial intelligence tools and their capabilities. PLOMS AI 1(1)"},{"key":"9187_CR34","unstructured":"Esmaeel AA (2018) A novel approach to classify and detect bean diseases based on image processing. In: 2018 IEEE symposium on computer applications industrial electronics (ISCAIE). IEEE, pp 297\u2013302"},{"key":"9187_CR35","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, 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":"9187_CR36","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"9187_CR37","doi-asserted-by":"crossref","unstructured":"He K, Zhang, 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":"9187_CR38","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, SunJ (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848\u20136856. https:\/\/fr.overleaf.com\/project\/63ecec5dc17cb2231ed1e645","DOI":"10.1109\/CVPR.2018.00716"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09187-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09187-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09187-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T13:17:23Z","timestamp":1704979043000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09187-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"references-count":38,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["9187"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09187-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,15]]},"assertion":[{"value":"18 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 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"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}