{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T21:07:49Z","timestamp":1770844069776,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T00:00:00Z","timestamp":1650240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T00:00:00Z","timestamp":1650240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00521-022-07213-5","type":"journal-article","created":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T11:12:49Z","timestamp":1650280369000},"page":"13925-13935","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Pre-trained deep learning-based classification of jujube fruits according to their maturity level"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8220-1708","authenticated-orcid":false,"given":"Atif","family":"Mahmood","sequence":"first","affiliation":[]},{"given":"Sanjay Kumar","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Amod Kumar","family":"Tiwari","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,18]]},"reference":[{"issue":"6","key":"7213_CR1","doi-asserted-by":"publisher","first-page":"463","DOI":"10.9755\/ejfa.v2516.15552","volume":"25","author":"S Pareek","year":"2013","unstructured":"Pareek S (2013) Nutritional composition of jujube fruit. Emirates J Food Agric 25(6):463\u2013470. https:\/\/doi.org\/10.9755\/ejfa.v2516.15552","journal-title":"Emirates J Food Agric"},{"issue":"866","key":"7213_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jff.2020.104205","volume":"75","author":"AK Rashwan","year":"2020","unstructured":"Rashwan AK, Karim N, Rezaul M, Shishir I, Bao T, Lu Y (2020) Jujube fruit: apotential nutritious fruit for the development of functional food products. J Funct Foods 75(866):104205. https:\/\/doi.org\/10.1016\/j.jff.2020.104205","journal-title":"J Funct Foods"},{"issue":"10","key":"7213_CR3","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1007\/s11947-015-1568-y","volume":"8","author":"LS Magwaza","year":"2015","unstructured":"Magwaza LS, Tesfay SZ (2015) A review of destructive and non-destructive methods for determining avocado fruit maturity. Food Bioprocess Technol 8(10):1995\u20132011. https:\/\/doi.org\/10.1007\/s11947-015-1568-y","journal-title":"Food Bioprocess Technol"},{"issue":"1","key":"7213_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/plants7010003","volume":"7","author":"B Li","year":"2018","unstructured":"Li B, Lecourt J, Bishop G (2018) Advances in non-destructive early assessment of fruit ripeness towards defining optimal time of harvest and yield prediction\u2013a review. Plants 7(1):1\u201320. https:\/\/doi.org\/10.3390\/plants7010003","journal-title":"Plants"},{"issue":"3","key":"7213_CR5","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.jksuci.2018.06.002","volume":"33","author":"A Bhargava","year":"2021","unstructured":"Bhargava A, Bansal A (2021) Fruits and vegetables quality evaluation using computer vision: a review. J King Saud Univ-Comput Inf Sci 33(3):243\u2013257. https:\/\/doi.org\/10.1016\/j.jksuci.2018.06.002","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"7213_CR6","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.crfs.2021.03.009","volume":"4","author":"L Zhu","year":"2021","unstructured":"Zhu L, Spachos P, Pensini E, Plataniotis KN (2021) Deep learning and machine vision for food processing: a survey. Curr Res Food Sci 4:233\u2013249. https:\/\/doi.org\/10.1016\/j.crfs.2021.03.009","journal-title":"Curr Res Food Sci"},{"key":"7213_CR7","unstructured":"Krizhevsky BA, Sutskever I, Hinton GE, ImageNet classification with deep convolutional neural networks, 2012."},{"key":"7213_CR8","first-page":"1","volume":"2015","author":"K Simonyan","year":"2015","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. ICLR 2015:1\u201314","journal-title":"ICLR"},{"key":"7213_CR9","doi-asserted-by":"publisher","unstructured":"Mahendran R, Gc J, Alagusundaram K (2012), Application of computer vision technique on sorting and grading of fruits and vegetables, 1\u20137, https:\/\/doi.org\/10.4172\/2157-7110.S1-001.","DOI":"10.4172\/2157-7110.S1-001"},{"issue":"5","key":"7213_CR10","doi-asserted-by":"publisher","first-page":"2042","DOI":"10.1002\/jsfa.10824","volume":"101","author":"RP Ramos","year":"2021","unstructured":"Ramos RP et al (2021) Non-invasive setup for grape maturation classification using deep learning. J Sci Food Agric 101(5):2042\u20132051. https:\/\/doi.org\/10.1002\/jsfa.10824","journal-title":"J Sci Food Agric"},{"issue":"1","key":"7213_CR11","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.jksuci.2010.03.003","volume":"23","author":"YA Ohali","year":"2011","unstructured":"Ohali YA (2011) Computer vision based date fruit grading system: design and implementation. J King Saud Univ-Comput Inf Sci 23(1):29\u201336. https:\/\/doi.org\/10.1016\/j.jksuci.2010.03.003","journal-title":"J King Saud Univ-Comput Inf Sci"},{"issue":"1","key":"7213_CR12","doi-asserted-by":"publisher","first-page":"105","DOI":"10.13053\/rcs-121-1-9","volume":"121","author":"EM Lara-espinoza","year":"2016","unstructured":"Lara-espinoza EM, Trejo-duran M, Lizarraga-morales RA (2016) Determination of the ripeness state of guavas using an artificial neural network. Res Comput Sci 121(1):105\u2013111. https:\/\/doi.org\/10.13053\/rcs-121-1-9","journal-title":"Res Comput Sci"},{"key":"7213_CR13","doi-asserted-by":"publisher","unstructured":"Baz\u00e1n K, Avila-george H, Member S (2019), Classification of cape gooseberry fruit according to its level of ripeness using machine learning techniques and different color spaces, 27389\u201327400, https:\/\/doi.org\/10.1109\/ACCESS.2019.2898223.","DOI":"10.1109\/ACCESS.2019.2898223"},{"key":"7213_CR14","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001419510029","author":"Z Xu","year":"2019","unstructured":"Xu Z, Yuan P, Zhang Y, Deng X (2019) Watermelon ripeness detection via extreme learning machine with kernel principal component analysis based on acoustic signals. Int J Pattern Recognit Artif Intell. https:\/\/doi.org\/10.1142\/S0218001419510029","journal-title":"Int J Pattern Recognit Artif Intell"},{"issue":"2","key":"7213_CR15","doi-asserted-by":"publisher","first-page":"859","DOI":"10.11591\/ijeecs.v14.i2.pp859-868","volume":"14","author":"MF Mavi","year":"2019","unstructured":"Mavi MF, Husin Z, Ahmad RB, Yacob YM, Farook RSM, Tan WK (2019) Mango ripeness classification system using hybrid technique. Indones J Electr Eng Comput Sci 14(2):859\u2013868. https:\/\/doi.org\/10.11591\/ijeecs.v14.i2.pp859-868","journal-title":"Indones J Electr Eng Comput Sci"},{"key":"7213_CR16","doi-asserted-by":"crossref","unstructured":"Nandi CS, Tudu B, Koley C, Machine vision based techniques for automatic mango fruit sorting and grading based on maturity level and size, In: Sensing technology: current status and future trends II,27 smart sensors, Measurement and instrumentation, no. January, Springer International Publishing Switzerland, 2014.","DOI":"10.1007\/978-3-319-02315-1_2"},{"key":"7213_CR17","doi-asserted-by":"publisher","first-page":"6901","DOI":"10.1007\/s13369-018-03695-5","volume":"44","author":"FMA Mazen","year":"2019","unstructured":"Mazen FMA, Nashat AA (2019) Ripeness classification of bananas using an artificial neural network. Arab J Sci Eng 44:6901\u20136910. https:\/\/doi.org\/10.1007\/s13369-018-03695-5","journal-title":"Arab J Sci Eng"},{"key":"7213_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-03267-w","author":"N Saranya","year":"2021","unstructured":"Saranya N, Srinivasan N, Kumar SKP (2021) Banana ripeness stage identification: a deep learning approach. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-021-03267-w","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"7213_CR19","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.compag.2011.12.010","volume":"82","author":"O Mohammed","year":"2012","unstructured":"Mohammed O et al (2012) Classification of oil palm fresh fruit bunches based on their maturity using portable four-band sensor system. Comput Electron Agric 82:55\u201360. https:\/\/doi.org\/10.1016\/j.compag.2011.12.010","journal-title":"Comput Electron Agric"},{"key":"7213_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s11694-021-01010-9","author":"J Ni","year":"2021","unstructured":"Ni J, Gao J, Li J, Yang H, Hao Z, Han Z (2021) E-AlexNet: quality evaluation of strawberry based on machine learning. J Food Meas Charact. https:\/\/doi.org\/10.1007\/s11694-021-01010-9","journal-title":"J Food Meas Charact"},{"key":"7213_CR21","doi-asserted-by":"publisher","unstructured":"Kumari S, Kumar A, Kumar P (2020), Maturity status classification of papaya fruits based on machine learning and transfer learning approach, Inf. Process. Agric., no. xxxx, https:\/\/doi.org\/10.1016\/j.inpa.2020.05.003.","DOI":"10.1016\/j.inpa.2020.05.003"},{"key":"7213_CR22","doi-asserted-by":"crossref","unstructured":"Tabik S and Peralta D (2017), A snapshot of image pre-processing for convolutional neural networks: case study of MNIST, 10, 555\u2013568.","DOI":"10.2991\/ijcis.2017.10.1.38"},{"key":"7213_CR23","unstructured":"Solem JE, Programming computer vision with python. 2012."},{"issue":"1","key":"7213_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1021\/ci0342472","volume":"44","author":"DM Hawkins","year":"2004","unstructured":"Hawkins DM (2004) The problem of overfitting. J Chem Inf Comput Sci 44(1):1\u201312. https:\/\/doi.org\/10.1021\/ci0342472","journal-title":"J Chem Inf Comput Sci"},{"key":"7213_CR25","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data. https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J Big Data"},{"key":"7213_CR26","unstructured":"Sharif A, Hossein R, Josephine A, Stefan S and Royal KTH, CNN features off-the-shelf: an astounding baseline for recognition.\u201d"},{"key":"7213_CR27","unstructured":"Salvador A, Zeppelzauer M, Manch D, Calafell A, Politecnica U and Upc DC, Cultural event recognition with visual convnets and temporal models."},{"key":"7213_CR28","doi-asserted-by":"publisher","unstructured":"B. C. B, V. M. B, O. Beijbom, Hoffman J and Darrell T (2016), Best practices for fine-tuning visual classifiers, pp. 435\u2013442, https:\/\/doi.org\/10.1007\/978-3-319-49409-8.","DOI":"10.1007\/978-3-319-49409-8"},{"key":"7213_CR29","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2021.1921037","author":"JV Roshan","year":"2021","unstructured":"Roshan JV, Akhil V (2021) SPlit: an optimal method for data splitting. Technometrics. https:\/\/doi.org\/10.1080\/00401706.2021.1921037","journal-title":"Technometrics"},{"key":"7213_CR30","unstructured":"Rahimi A, Banakar A and Zareiforoush H (2014), Classification of jujube fruits using different data mining methods, no. January. 52\u201361."},{"issue":"7","key":"7213_CR31","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.3964\/j.issn.1000-0593(2018)07-2175-08","volume":"38","author":"SB Cao Xiao-feng","year":"2018","unstructured":"Cao Xiao-feng SB, Hui-ru REN, Xing-zhi LI, Ke-qiang YU (2018) Discrimination of winter jujube\u2019s maturity using hyperspectral technique combined with characteristic wavelength and spectral indices. Spectrosc Spectr Anal 38(7):2175\u20132182. https:\/\/doi.org\/10.3964\/j.issn.1000-0593(2018)07-2175-08","journal-title":"Spectrosc Spectr Anal"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07213-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07213-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07213-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T10:16:28Z","timestamp":1658657788000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07213-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,18]]},"references-count":31,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["7213"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07213-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,18]]},"assertion":[{"value":"15 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2022","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"}}]}}