{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T13:34:23Z","timestamp":1775741663117,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"32","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004781","name":"Institut Pengurusan dan Pemantauan Penyelidikan, Universiti Malaya","doi-asserted-by":"publisher","award":["PPSI-2020-CLUSTER-SD01"],"award-info":[{"award-number":["PPSI-2020-CLUSTER-SD01"]}],"id":[{"id":"10.13039\/501100004781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s00521-024-10300-4","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T07:02:45Z","timestamp":1723446165000},"page":"20473-20491","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improved hybrid feature extractor in lightweight convolutional neural network for postharvesting technology: automated oil palm fruit grading"],"prefix":"10.1007","volume":"36","author":[{"given":"Mohamad Haniff","family":"Junos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9873-4779","authenticated-orcid":false,"given":"Anis Salwa","family":"Mohd Khairuddin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamad Sofian","family":"Abu Talip","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Izhar","family":"Kairi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yosri Mohd","family":"Siran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"10300_CR1","first-page":"377","volume-title":"palm oil","author":"OM Lai","year":"2012","unstructured":"Lai OM, Tan CP, Akoh CC (2012) The physicochemical properties of palm oil and its components. In: Lai O-M, Tan C-P, Akoh CC (eds) palm oil. AOCS Press, Illinois, pp 377\u2013391"},{"key":"10300_CR2","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1016\/j.jfoodeng.2005.12.049","volume":"78","author":"OK Owolarafe","year":"2007","unstructured":"Owolarafe OK, Olabige MT, Faborode MO (2007) Physical and mechanical properties of two varieties of fresh oil palm fruit. J Food Eng 78:1228\u20131232. https:\/\/doi.org\/10.1016\/j.jfoodeng.2005.12.049","journal-title":"J Food Eng"},{"key":"10300_CR3","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1016\/j.tree.2008.06.012","volume":"23","author":"EB Fitzherbert","year":"2008","unstructured":"Fitzherbert EB, Struebig MJ, Morel A, Danielsen F, Br\u00fchl CA, Donald PF, Phalan B (2008) How will oil palm expansion affect biodiversity? Trends Ecol Evol 23:538\u2013545. https:\/\/doi.org\/10.1016\/j.tree.2008.06.012","journal-title":"Trends Ecol Evol"},{"key":"10300_CR4","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.compag.2015.09.018","volume":"118","author":"A Taparugssanagorn","year":"2015","unstructured":"Taparugssanagorn A, Siwamogsatham S, Pomalaza-R\u00e1ez C (2015) A non-destructive oil palm ripeness recognition system using relative entropy. Comput Electron Agric 118:340\u2013349. https:\/\/doi.org\/10.1016\/j.compag.2015.09.018","journal-title":"Comput Electron Agric"},{"key":"10300_CR5","doi-asserted-by":"publisher","first-page":"7","DOI":"10.2202\/1556-3758.1090","volume":"2","author":"TSY Choong","year":"2006","unstructured":"Choong TSY, Abbas S, Shariff AR, Halim R, Ismail MHS, Yunus R, Ali S, Ahmadun FR (2006) Digital image processing of palm oil fruits. Int J Food Eng 2:7\u201312. https:\/\/doi.org\/10.2202\/1556-3758.1090","journal-title":"Int J Food Eng"},{"key":"10300_CR6","first-page":"653","volume":"1","author":"AH Hitam","year":"2000","unstructured":"Hitam AH, Yusof AM (2000) Mechanization in oil palm platations. Adv Oil Palm Res 1:653\u2013696","journal-title":"Adv Oil Palm Res"},{"key":"10300_CR7","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.indcrop.2011.10.020","volume":"36","author":"MHM Hazir","year":"2012","unstructured":"Hazir MHM, Shariff ARM, Amiruddin MD (2012) Determination of oil palm fresh fruit bunch ripeness-Based on flavonoids and anthocyanin content. Ind Crops Prod 36:466\u2013475. https:\/\/doi.org\/10.1016\/j.indcrop.2011.10.020","journal-title":"Ind Crops Prod"},{"key":"10300_CR8","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.compag.2011.12.010","volume":"82","author":"SOM Ben","year":"2012","unstructured":"Ben SOM, Sankaran S, Shariff ARM, Shafri HZM, Ehsani R, Alfatni MS, Hazir MHM (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":"10300_CR9","doi-asserted-by":"publisher","first-page":"1128","DOI":"10.3390\/app90611288","volume":"9","author":"Y Li","year":"2019","unstructured":"Li Y, Hu W, Dong H, Zhang X (2019) Building damage detection from post-event aerial imagery using single shot multibox detector. Appl Sci 9:1128. https:\/\/doi.org\/10.3390\/app90611288","journal-title":"Appl Sci"},{"key":"10300_CR10","doi-asserted-by":"publisher","first-page":"3781","DOI":"10.3390\/app9183781","volume":"9","author":"Y Li","year":"2019","unstructured":"Li Y, Han Z, Xu H, Liu L, Li X, Zhang K (2019) YOLOv3-lite: A lightweight crack detection network for aircraft structure based on depthwise separable convolutions. Appl Sci 9:3781. https:\/\/doi.org\/10.3390\/app9183781","journal-title":"Appl Sci"},{"key":"10300_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-020-01831-7","author":"W Chen","year":"2020","unstructured":"Chen W, Huang H, Peng S, Zhou C, Zhang C (2020) YOLO-face: a real-time face detector. Vis Comput. https:\/\/doi.org\/10.1007\/s00371-020-01831-7","journal-title":"Vis Comput"},{"key":"10300_CR12","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.patrec.2020.03.002","volume":"133","author":"J Luo","year":"2020","unstructured":"Luo J, Liu J, Lin J, Wang Z (2020) A lightweight face detector by integrating the convolutional neural network with the image pyramid. Pattern Recognit Lett 133:180\u2013187. https:\/\/doi.org\/10.1016\/j.patrec.2020.03.002","journal-title":"Pattern Recognit Lett"},{"key":"10300_CR13","doi-asserted-by":"publisher","first-page":"114417","DOI":"10.1016\/j.eswa.2020.114417","volume":"169","author":"X Yuan","year":"2021","unstructured":"Yuan X, Shi J, Gu L (2021) A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Syst Appl 169:114417. https:\/\/doi.org\/10.1016\/j.eswa.2020.114417","journal-title":"Expert Syst Appl"},{"key":"10300_CR14","doi-asserted-by":"publisher","first-page":"6023","DOI":"10.1016\/j.aej.2021.11.027","volume":"61","author":"MH Junos","year":"2022","unstructured":"Junos MH, MohdKhairuddin AS, Dahari M (2022) Automated object detection on aerial images for limited capacity embedded device using a lightweight CNN model. Alexandria Eng J 61:6023\u20136041. https:\/\/doi.org\/10.1016\/j.aej.2021.11.027","journal-title":"Alexandria Eng J"},{"key":"10300_CR15","doi-asserted-by":"publisher","first-page":"1869","DOI":"10.1007\/s00371-019-01775-7","volume":"36","author":"P Xi","year":"2020","unstructured":"Xi P, Guan H, Shu C et al (2020) An integrated approach for medical abnormality detection using deep patch convolutional neural networks. Vis Comput 36:1869\u20131882. https:\/\/doi.org\/10.1007\/s00371-019-01775-7","journal-title":"Vis Comput"},{"key":"10300_CR16","doi-asserted-by":"publisher","DOI":"10.3390\/rs12121994","author":"X Luo","year":"2020","unstructured":"Luo X, Tian X, Zhang H, Hou W, Leng G, Xu W, He X, Wang M, Zhang J (2020) Fast automatic vehicle detection in UAV images using convolutional neural networks. Remote Sens. https:\/\/doi.org\/10.3390\/rs12121994","journal-title":"Remote Sens"},{"key":"10300_CR17","doi-asserted-by":"publisher","first-page":"12073","DOI":"10.1007\/s00521-023-08340-3","volume":"35","author":"S Adige","year":"2023","unstructured":"Adige S, Kurban R, Durmu\u015f A, Karak\u00f6se E (2023) Classification of apple images using support vector machines and deep residual networks. Neural Comput Appl 35:12073\u201312087. https:\/\/doi.org\/10.1007\/s00521-023-08340-3","journal-title":"Neural Comput Appl"},{"key":"10300_CR18","doi-asserted-by":"publisher","first-page":"104712","DOI":"10.1016\/j.engappai.2021.104172","volume":"100","author":"P Tong","year":"2021","unstructured":"Tong P, Han P, Li S, Li S, Li N, Bu S, Li Q, Li K (2021) Counting trees with point-wise supervised segmentation network. Eng Appl Artif Intell 100:104712. https:\/\/doi.org\/10.1016\/j.engappai.2021.104172","journal-title":"Eng Appl Artif Intell"},{"key":"10300_CR19","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12181","author":"MH Junos","year":"2021","unstructured":"Junos MH, MohdKhairuddin AS, Thannirmalai S, Dahari M (2021) An optimized YOLO-based object detection model for crop harvesting system. IET Image Process. https:\/\/doi.org\/10.1049\/ipr2.12181","journal-title":"IET Image Process"},{"key":"10300_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-021-02116-3","author":"MH Junos","year":"2021","unstructured":"Junos MH, MohdKhairuddin AS, Thannirmalai S, Dahari M (2021) Automatic detection of oil palm fruits from UAV images using an improved YOLO model. Vis Comput. https:\/\/doi.org\/10.1007\/s00371-021-02116-3","journal-title":"Vis Comput"},{"key":"10300_CR21","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1007\/s11119-018-9614-1","volume":"20","author":"NT Anderson","year":"2018","unstructured":"Anderson NT, Underwood JP, Rahman MM, Robson A, Walsh KB (2018) Estimation of fruit load in mango orchards :  tree sampling considerations and use of machine vision and satellite imagery. Precis Agric 20:823\u2013839. https:\/\/doi.org\/10.1007\/s11119-018-9614-1","journal-title":"Precis Agric"},{"key":"10300_CR22","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1007\/s10489-021-02452-w","volume":"52","author":"SF Syed-Ab-Rahman","year":"2022","unstructured":"Syed-Ab-Rahman SF, Hesamian MH, Prasad M (2022) Citrus disease detection and classification using end-to-end anchor-based deep learning model. Appl Intell 52:927\u2013938","journal-title":"Appl Intell"},{"key":"10300_CR23","doi-asserted-by":"publisher","first-page":"14855","DOI":"10.1007\/s00521-023-08496-y","volume":"35","author":"P Hari","year":"2023","unstructured":"Hari P, Prasad Sigh M (2023) A lightweight convolutional neural network for disease detection of fruit leaves. Neural Comput Appl 35:14855\u201314866. https:\/\/doi.org\/10.1007\/s00521-023-08496-y","journal-title":"Neural Comput Appl"},{"key":"10300_CR24","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.compag.2019.01.012","volume":"157","author":"Y Tian","year":"2019","unstructured":"Tian Y, Yang G, Wang Z, Wang H, Li E, Liang Z (2019) Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Comput Electron Agric 157:417\u2013426. https:\/\/doi.org\/10.1016\/j.compag.2019.01.012","journal-title":"Comput Electron Agric"},{"key":"10300_CR25","doi-asserted-by":"publisher","first-page":"110245","DOI":"10.1016\/j.scienta.2021.110245","volume":"286","author":"A Septiarini","year":"2021","unstructured":"Septiarini A, Sunyoto A, Hamdani H, Kasim AA, Utaminingrum F, Hatta HR (2021) Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features. Sci Hortic 286:110245. https:\/\/doi.org\/10.1016\/j.scienta.2021.110245","journal-title":"Sci Hortic"},{"key":"10300_CR26","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1007\/s00521-021-06473-x","volume":"34","author":"N Kumari","year":"2022","unstructured":"Kumari N, Kr. Dwivedi R, Kr. Bhatt A, Belwal R, (2022) Automated fruit grading using optimal feature selection and hybrid classification by self-adaptive chicken swarm optimization: grading of mango. Neural Comput Appl 34:1285\u20131306. https:\/\/doi.org\/10.1007\/s00521-021-06473-x","journal-title":"Neural Comput Appl"},{"key":"10300_CR27","doi-asserted-by":"publisher","unstructured":"Shabdin MK, Shariff ARM, Johari MNA, Saat NK, Abbas Z (2016) A study on the oil palm fresh fruit bunch (FFB) ripeness detection by using Hue, Saturation and Intensity (HSI) approach. In: IOP conference series: earth and environmental science. pp 37. https:\/\/doi.org\/10.1088\/1755-1315\/37\/1\/012039","DOI":"10.1088\/1755-1315\/37\/1\/012039"},{"key":"10300_CR28","doi-asserted-by":"publisher","unstructured":"Septiarini A, Hatta HR, Hamdani H, Oktavia A, Kasim AA, Suyanto S (2020) Maturity grading of oil palm fresh fruit bunches based on a machine learning approach. In: 2020 5th International conference on informatics and computing (ICIC). pp 6\u20139. https:\/\/doi.org\/10.1109\/ICIC50835.2020.9288603","DOI":"10.1109\/ICIC50835.2020.9288603"},{"key":"10300_CR29","doi-asserted-by":"publisher","first-page":"563","DOI":"10.4314\/jfas.v9i4s.32","volume":"9","author":"N Sabri","year":"2018","unstructured":"Sabri N, Ibrahim Z, Syahlan S, Jamil N, Mangshor NNA (2018) Palm oil fresh fruit bunch ripeness grading identification using color features. J Fundam Appl Sci 9:563. https:\/\/doi.org\/10.4314\/jfas.v9i4s.32","journal-title":"J Fundam Appl Sci"},{"key":"10300_CR30","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.compag.2013.02.008","volume":"93","author":"M Makky","year":"2013","unstructured":"Makky M, Soni P (2013) Development of an automatic grading machine for oil palm fresh fruits bunches (FFBs) based on machine vision. Comput Electron Agric 93:129\u2013139. https:\/\/doi.org\/10.1016\/j.compag.2013.02.008","journal-title":"Comput Electron Agric"},{"key":"10300_CR31","doi-asserted-by":"publisher","unstructured":"Alfatni MSM, Mohamed Shariff AR, Bejo SK, Ben Saeed OM, Mustapha A (2018) Real-time oil palm FFB ripeness grading system based on ANN, KNN and SVM classifiers. In: IOP Conference series: earth and environmental science. pp 169. https:\/\/doi.org\/10.1088\/1755-1315\/169\/1\/012067","DOI":"10.1088\/1755-1315\/169\/1\/012067"},{"key":"10300_CR32","doi-asserted-by":"publisher","unstructured":"Harsawardana, Rahutomo R, Mahesworo B, Cenggoro TW, Budiarto A, Surya Atmaja DB, Samoedro B, Pardamean B (2020) AI-based ripeness grading for oil palm fresh fruit bunch in smart crane grabber. In: IOP Conference series: earth and environmental science. pp 426. https:\/\/doi.org\/10.1088\/1755-1315\/426\/1\/012147","DOI":"10.1088\/1755-1315\/426\/1\/012147"},{"key":"10300_CR33","doi-asserted-by":"publisher","unstructured":"Bensaeed OM, Shariff AM, Mahmud AB, Shafri H, Alfatni M (2014) Oil palm fruit grading using a hyperspectral device and machine learning algorithm. In: IOP conference series: earth and environmental science. pp 20. https:\/\/doi.org\/10.1088\/1755-1315\/20\/1\/012017","DOI":"10.1088\/1755-1315\/20\/1\/012017"},{"key":"10300_CR34","doi-asserted-by":"publisher","first-page":"130","DOI":"10.3173\/air.18.130","volume":"18","author":"P Junkwon","year":"2009","unstructured":"Junkwon P, Takigawa T, Okamoto H, Hasegawa H, Koike M, Sakai K, Siruntawineti J, Chaeychomsri W, Vanavichit A, Tittinuchanon P, Bahalayodhin B (2009) Hyperspectral imaging for nondestructive determination of internal qualities for oil palm (Elaeis guineensis Jacq. var. tenera). Agric Inf Res 18:130\u2013141. https:\/\/doi.org\/10.3173\/air.18.130","journal-title":"Agric Inf Res"},{"key":"10300_CR35","doi-asserted-by":"publisher","unstructured":"Setiawan AW, Prasetya OE (2020) Palm oil fresh fruit bunch grading system using multispectral image analysis in HSV. In: 2020 IEEE international international conference on informatics, IoT, enabling technologies (ICIoT). pp 85\u201388. https:\/\/doi.org\/10.1109\/ICIoT48696.2020.9089431","DOI":"10.1109\/ICIoT48696.2020.9089431"},{"key":"10300_CR36","first-page":"84","volume":"60","author":"BA Krizhevsky","year":"2012","unstructured":"Krizhevsky BA, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 60:84\u201390","journal-title":"Adv Neural Inf Process Syst"},{"key":"10300_CR37","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations. pp 1\u201314"},{"key":"10300_CR38","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR). Las Vegas, NV, USA","DOI":"10.1109\/CVPR.2016.90"},{"key":"10300_CR39","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten L van der, Weinberger KQ (2017) Densely connected convolutional networks. In: 30th IEEE conference on computer vision and pattern recognition (CVPR). pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"10300_CR40","doi-asserted-by":"publisher","first-page":"2380","DOI":"10.1016\/j.procs.2020.04.258","volume":"171","author":"MM Raikar","year":"2020","unstructured":"Raikar MM, Meena SM, Kuchanur C, Girraddi S, Benagi P (2020) Classification and grading of okra-ladies finger using deep learning. Procedia Comput Sci 171:2380\u20132389. https:\/\/doi.org\/10.1016\/j.procs.2020.04.258","journal-title":"Procedia Comput Sci"},{"key":"10300_CR41","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/551636","author":"A Helwan","year":"2021","unstructured":"Helwan A, SallamMa\u2019Aitah MK, Abiyev RH, Uzelaltinbulat S, Sonyel B (2021) Deep learning based on residual networks for automatic sorting of bananas. J Food Qual. https:\/\/doi.org\/10.1155\/2021\/551636","journal-title":"J Food Qual"},{"key":"10300_CR42","doi-asserted-by":"crossref","unstructured":"Wu S, Tung H, Hsu Y (2020) Deep learning for automatic quality grading of mangoes Classification: lazy KStar classifier:  Methods and insights. In: 19th IEEE international conference on machine learning and applications (ICMLA). pp 446\u2013453","DOI":"10.1109\/ICMLA51294.2020.00076"},{"key":"10300_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.inpa.2021.01.005","author":"N Ismail","year":"2021","unstructured":"Ismail N, Malik OA (2021) Real-time visual inspection system for grading fruits using computer vision and deep learning techniques. Inf Process Agric. https:\/\/doi.org\/10.1016\/j.inpa.2021.01.005","journal-title":"Inf Process Agric"},{"key":"10300_CR44","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture11080687","author":"AR Mesa","year":"2021","unstructured":"Mesa AR, Chiang JY (2021) Multi-input deep learning model with rgb and hyperspectral imaging for banana grading. Agric. https:\/\/doi.org\/10.3390\/agriculture11080687","journal-title":"Agric"},{"key":"10300_CR45","doi-asserted-by":"publisher","first-page":"109133","DOI":"10.1016\/j.scienta.2019.109133","volume":"263","author":"A Jahanbakhshi","year":"2020","unstructured":"Jahanbakhshi A, Momeny M, Mahmoudi M, Zhang YD (2020) Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks. Sci Hortic 263:109133. https:\/\/doi.org\/10.1016\/j.scienta.2019.109133","journal-title":"Sci Hortic"},{"key":"10300_CR46","doi-asserted-by":"publisher","first-page":"111204","DOI":"10.1016\/j.postharvbio.2020.111204","volume":"166","author":"M Momeny","year":"2020","unstructured":"Momeny M, Jahanbakhshi A, Jafarnezhad K, Zhang YD (2020) Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach. Postharvest Biol Technol 166:111204. https:\/\/doi.org\/10.1016\/j.postharvbio.2020.111204","journal-title":"Postharvest Biol Technol"},{"key":"10300_CR47","doi-asserted-by":"publisher","first-page":"110922","DOI":"10.1016\/j.postharvbio.2019.05.023","volume":"156","author":"TT Le","year":"2019","unstructured":"Le TT, Lin CY, Piedad EJ (2019) Deep learning for noninvasive classification of clustered horticultural crops\u2014a case for banana fruit tiers. Postharvest Biol Technol 156:110922. https:\/\/doi.org\/10.1016\/j.postharvbio.2019.05.023","journal-title":"Postharvest Biol Technol"},{"key":"10300_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-023-04799-8","author":"B Xiao","year":"2023","unstructured":"Xiao B, Nguyen M, Qi W (2023) Fruit ripeness identification using transformers. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-023-04799-8","journal-title":"Appl Intell"},{"issue":"19","key":"10300_CR49","doi-asserted-by":"publisher","first-page":"3150","DOI":"10.3390\/foods11193150","volume":"11","author":"X Liang","year":"2022","unstructured":"Liang X, Jia X, Huang W et al (2022) Real-time grading of defect apples using semantic segmentation combination with a pruned YOLO V4 network. Foods 11(19):3150. https:\/\/doi.org\/10.3390\/foods11193150","journal-title":"Foods"},{"key":"10300_CR50","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1038\/s41597-023-01958-x","volume":"10","author":"FA Junior","year":"2023","unstructured":"Junior FA, Koeswandy YP, Nurhayati PW, Asrol M (2023) Annotated datasets of oil palm fruit bunch piles for ripeness grading using deep learning. Sci Data 10:72. https:\/\/doi.org\/10.1038\/s41597-023-01958-x","journal-title":"Sci Data"},{"key":"10300_CR51","doi-asserted-by":"publisher","first-page":"59758","DOI":"10.1109\/ACCESS.2023.3285537","volume":"11","author":"M Asrol","year":"2023","unstructured":"Asrol M, Utama DN, JuniorMarimin FA (2023) Real-time oil palm fruit grading system using smartphone and modified YOLOv4. IEEE Access 11:59758\u201359773. https:\/\/doi.org\/10.1109\/ACCESS.2023.3285537","journal-title":"IEEE Access"},{"key":"10300_CR52","unstructured":"Tan M, Le Q V. (2019) EfficientNet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning (ICML). pp 10691\u201310700"},{"key":"10300_CR53","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Andrey Z, Liang-Chieh C (2018) MobileNetV2: Inverted residuals and linear bottlenecks. In: IEEE\/CVF conference on computer vision and pattern Recognition. pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10300_CR54","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/S0020-0255(96)00200-9","volume":"99","author":"S Narayan","year":"1997","unstructured":"Narayan S (1997) The generalized sigmoid activation function: Competitive supervised learning. Inf Sci 99:69\u201382. https:\/\/doi.org\/10.1016\/S0020-0255(96)00200-9","journal-title":"Inf Sci"},{"key":"10300_CR55","doi-asserted-by":"crossref","unstructured":"Lin T, Dollar P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2017.106"},{"key":"10300_CR56","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1007\/978-3-319-10578-9_23","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37:1904\u20131916. https:\/\/doi.org\/10.1007\/978-3-319-10578-9_23","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10300_CR57","doi-asserted-by":"publisher","first-page":"23729","DOI":"10.1007\/s11042-020-08976-6","volume":"79","author":"Y Xiao","year":"2020","unstructured":"Xiao Y, Tian Z, Yu J, Zhang Y, Liu S, Du S, Lan X (2020) A review of object detection based on deep learning. Multimed Tools Appl 79:23729\u201323791. https:\/\/doi.org\/10.1007\/s11042-020-08976-6","journal-title":"Multimed Tools Appl"},{"key":"10300_CR58","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Bourdev L, Girshick R, Hays J, Perona P, Ramanan D, Zitnick CL, Dollar P (2014) Microsoft COCO: Common objects in context. In: Computer vision\u2014ECCV 2014. Lecture Notes in Computer Science. pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"10300_CR59","doi-asserted-by":"publisher","unstructured":"Tuerxun A, Mohamed Shariff AR, Janius R, Abbas Zu, Mahdiraji GA (2020) Oil palm fresh fruit bunches maturity prediction by using optical spectrometer. In: IOP conference series: earth and environmental science. pp 540. https:\/\/doi.org\/10.1088\/1755-1315\/540\/1\/012085","DOI":"10.1088\/1755-1315\/540\/1\/012085"},{"key":"10300_CR60","doi-asserted-by":"publisher","first-page":"95763","DOI":"10.1109\/ACCESS.2022.3204762","volume":"10","author":"JW Lai","year":"2022","unstructured":"Lai JW, Ramli HR, Ismail LI, Hasan WZW (2022) Real-time detection of ripe oil palm fresh fruit bunch based on YOLOv4. IEEE Access 10:95763\u201395770. https:\/\/doi.org\/10.1109\/ACCESS.2022.3204762","journal-title":"IEEE Access"},{"key":"10300_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.107864","author":"P Pipitsunthonsan","year":"2023","unstructured":"Pipitsunthonsan P, Pan L, Peng S, Khaorapapong T, Nakasathien S, Channumsin S, Chongcheawchamnan M (2023) Palm bunch grading technique using a multi-input and multi-label convolutional neural network. Comput Electron Agric. https:\/\/doi.org\/10.1016\/j.compag.2023.107864","journal-title":"Comput Electron Agric"},{"key":"10300_CR62","unstructured":"Zhu P, Wen L, Du D, Bian X, Hu Q, Ling H (2020) Vision meets drones: Past, present and future. In: Computer vision and pattern recognition. pp 1\u201320"},{"key":"10300_CR63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00011","author":"P Zhang","year":"2019","unstructured":"Zhang P, Zhong Y, Li X (2019) SlimYOLOv3: Narrower, faster and better for real-time UAV applications. Int Conf Comput Vis Work. https:\/\/doi.org\/10.1109\/ICCVW.2019.00011","journal-title":"Int Conf Comput Vis Work"},{"key":"10300_CR64","doi-asserted-by":"publisher","first-page":"103058","DOI":"10.1016\/j.jvcir.2021.103058","volume":"77","author":"Z Li","year":"2021","unstructured":"Li Z, Liu X, Zhao Y, Liu B, Huang Z, Hong R (2021) A lightweight multi-scale aggregated model for detecting aerial images captured by UAVs. J Vis Commun Image Represent 77:103058. https:\/\/doi.org\/10.1016\/j.jvcir.2021.103058","journal-title":"J Vis Commun Image Represent"},{"key":"10300_CR65","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.3390\/s20071861","volume":"20","author":"H Zhao","year":"2020","unstructured":"Zhao H, Zhou Y, Zhang L, Peng Y, Hu X, Peng H, Cai X (2020) Mixed YOLOv3-LITE: a lightweight real-time object detection method. Sensors 20:1861. https:\/\/doi.org\/10.3390\/s20071861","journal-title":"Sensors"},{"key":"10300_CR66","doi-asserted-by":"publisher","first-page":"113977","DOI":"10.1016\/j.eswa.2020.113977","volume":"164","author":"Y Ko\u00e7ak","year":"2021","unstructured":"Ko\u00e7ak Y, \u00dcst\u00fcnda\u011f\u015eiray G (2021) New activation functions for single layer feedforward neural network. Expert Syst Appl 164:113977. https:\/\/doi.org\/10.1016\/j.eswa.2020.113977","journal-title":"Expert Syst Appl"}],"updated-by":[{"DOI":"10.1007\/s00521-025-11303-5","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T00:00:00Z","timestamp":1747440000000}}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10300-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10300-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10300-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T09:16:38Z","timestamp":1747473398000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10300-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,12]]},"references-count":66,"journal-issue":{"issue":"32","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10300"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10300-4","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s00521-025-11303-5","asserted-by":"object"}]},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,12]]},"assertion":[{"value":"1 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2025","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00521-025-11303-5","URL":"https:\/\/doi.org\/10.1007\/s00521-025-11303-5","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. All the authors listed have approved the manuscript as enclosed.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}