{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T17:51:31Z","timestamp":1780595491948,"version":"3.54.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T00:00:00Z","timestamp":1618012800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T00:00:00Z","timestamp":1618012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"faculty grant","award":["GPF042A-2019"],"award-info":[{"award-number":["GPF042A-2019"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s00371-021-02116-3","type":"journal-article","created":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T07:02:45Z","timestamp":1618038165000},"page":"2341-2355","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Automatic detection of oil palm fruits from UAV images using an improved YOLO model"],"prefix":"10.1007","volume":"38","author":[{"given":"Mohamad Haniff","family":"Junos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anis Salwa","family":"Mohd Khairuddin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subbiah","family":"Thannirmalai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahidzal","family":"Dahari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,10]]},"reference":[{"key":"2116_CR1","unstructured":"MPOC: Malaysian Palm Oil Council, http:\/\/www.mpoc.org.my, accessed 15 September 2020"},{"key":"2116_CR2","unstructured":"Idrees, A.: Malaysia Palm Oil Industry, http:\/\/www.mpoc.org.my\/Malaysian_Palm_Oil_Industry.aspx, accessed 15 September 2020"},{"key":"2116_CR3","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.compag.2016.06.022","volume":"127","author":"Y Zhao","year":"2016","unstructured":"Zhao, Y., Gong, L., Huang, Y., Liu, C.: A review of key techniques of vision-based control for harvesting robot. Comput. Electron. Agric. 127, 311\u2013323 (2016)","journal-title":"Comput. Electron. Agric."},{"key":"2116_CR4","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1504\/IJCVR.2012.046419","volume":"3","author":"R Mairon","year":"2012","unstructured":"Mairon, R., Edan, Y.: Computer vision for fruit harvesting robots\u2014state of the art and challenges ahead. Int. J. Comput. Vis. Robot. 3, 4\u201334 (2012)","journal-title":"Int. J. Comput. Vis. Robot."},{"issue":"7","key":"2116_CR5","doi-asserted-by":"publisher","first-page":"12191","DOI":"10.3390\/s140712191","volume":"14","author":"K Yamamoto","year":"2014","unstructured":"Yamamoto, K., Guo, W., Yoshioka, Y., Ninomiya, S.: On plant detection of intact tomato fruits using image analysis and machine learning methods. Sensors 14(7), 12191\u201312206 (2014)","journal-title":"Sensors"},{"key":"2116_CR6","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1016\/j.compag.2016.07.023","volume":"127","author":"W Maldonado","year":"2016","unstructured":"Maldonado, W., Barbosa, J.C.: Automatic green fruit counting in orange trees using digital images. Comput. Electron. Agric. 127, 572\u2013581 (2016)","journal-title":"Comput. Electron. Agric."},{"key":"2116_CR7","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1007\/s11119-016-9458-5","volume":"18","author":"WS Qureshi","year":"2016","unstructured":"Qureshi, W.S., Payne, A., Walsh, K.B., Linker, R., Cohen, O., Dailey, M.N.: Machine vision for counting fruit on mango tree canopies. Precis. Agric. 18, 224\u2013244 (2016)","journal-title":"Precis. Agric."},{"key":"2116_CR8","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1049\/iet-ipr.2018.6524","volume":"14","author":"R Hamza","year":"2020","unstructured":"Hamza, R., Chtourou, M.: Design of fuzzy inference system for apple ripeness estimation using gradient method. IET Image Process. 14, 561\u2013569 (2020)","journal-title":"IET Image Process."},{"key":"2116_CR9","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","volume":"128","author":"L Liu","year":"2020","unstructured":"Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., Pietik\u00e4inen, M.: Deep learning for generic object detection: A survey. Int. J. Comput. Vis. 128, 261\u2013318 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"2116_CR10","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"ZQ Zhao","year":"2019","unstructured":"Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30, 3212\u20133232 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2116_CR11","doi-asserted-by":"publisher","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","volume":"7","author":"L Jiao","year":"2019","unstructured":"Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., Qu, R.: A survey of deep learning-based object detection. IEEE Access. 7, 128837\u2013128868 (2019)","journal-title":"IEEE Access."},{"key":"2116_CR12","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J., Berkeley, U.C.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"2116_CR13","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: IEEE International Conference on Computer Vision Fast, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"2116_CR14","first-page":"1","volume":"36","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN\u202f: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1\u201314 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2116_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"2116_CR16","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., Berg, A.C.: SSD\u202f: single shot multibox detector. In: European Conference on Computer Vision, pp. 21\u201337 (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2116_CR17","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once\u202f: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"2116_CR18","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: YOLO9000: Better, faster, stronger. In: IEEE conference on Computer Vision and Pattern Recognition, pp. 6517\u20136525 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"2116_CR19","unstructured":"Redmon, J., Farhadi, A.: YOLOv3\u202f: An incremental improvement. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)"},{"key":"2116_CR20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal Loss for Dense Object Detection. In: IEEE transactions on pattern analysis and machine intelligence. pp. 318\u2013327 (2020)","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"2116_CR21","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q. V.: EfficientDet: Scalable and efficient object detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp. 10778\u201310787 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"2116_CR22","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.: YOLOv3-lite: a lightweight crack detection network for aircraft structure based on depthwise separable convolutions. Appl. Sci. 9, 3781 (2019)","journal-title":"Appl. Sci."},{"key":"2116_CR23","doi-asserted-by":"publisher","first-page":"119096","DOI":"10.1016\/j.conbuildmat.2020.119096","volume":"252","author":"SE Park","year":"2020","unstructured":"Park, S.E., Eem, S.H., Jeon, H.: Concrete crack detection and quantification using deep learning and structured light. Constr. Build. Mater. 252, 119096 (2020)","journal-title":"Constr. Build. Mater."},{"key":"2116_CR24","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., Borgeat, L., Goubran, R.: An integrated approach for medical abnormality detection using deep patch convolutional neural networks. Vis. Comput. 36, 1869\u20131882 (2020)","journal-title":"Vis. Comput."},{"key":"2116_CR25","doi-asserted-by":"publisher","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Rajendra Acharya, U.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121, 103792 (2020)","journal-title":"Comput. Biol. Med."},{"key":"2116_CR26","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/s00371-018-01617-y","volume":"35","author":"M Villamizar","year":"2019","unstructured":"Villamizar, M., Sanfeliu, A., Moreno-Noguer, F.: Online learning and detection of faces with low human supervision. Vis. Comput. 35, 349\u2013370 (2019)","journal-title":"Vis. Comput."},{"key":"2116_CR27","first-page":"1","volume":"37","author":"W Chen","year":"2020","unstructured":"Chen, W., Huang, H., Peng, S., Zhou, C., Zhang, C.: YOLO-face: a real-time face detector. Vis. Comput. 37, 1\u20139 (2020)","journal-title":"Vis. Comput."},{"key":"2116_CR28","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1049\/iet-ipr.2018.6449","volume":"13","author":"W Min","year":"2019","unstructured":"Min, W., Li, X., Wang, Q., Zeng, Q., Liao, Y.: New approach to vehicle license plate location based on new model YOLO-L and plate pre-identification. IET Image Process. 13, 1041\u20131049 (2019)","journal-title":"IET Image Process."},{"key":"2116_CR29","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.imavis.2019.04.007","volume":"87","author":"RC Hendry","year":"2019","unstructured":"Hendry, R.C.: Automatic license plate recognition via sliding-window darknet-YOLO deep learning. Image Vis. Comput. 87, 47\u201356 (2019)","journal-title":"Image Vis. Comput."},{"key":"2116_CR30","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.imavis.2019.04.003","volume":"87","author":"E Lee","year":"2019","unstructured":"Lee, E., Kim, D.: Accurate traffic light detection using deep neural network with focal regression loss. Image Vis. Comput. 87, 24\u201336 (2019)","journal-title":"Image Vis. Comput."},{"key":"2116_CR31","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1109\/LRA.2017.2651944","volume":"2","author":"SW Chen","year":"2017","unstructured":"Chen, S.W., Shivakumar, S.S., Dcunha, S., Das, J., Okon, E., Qu, C., Taylor, C.J., Kumar, V.: Counting apples and oranges with deep learning\u202f: A data driven approach. IEEE Robot. Autom. Lett. 2, 781\u2013788 (2017)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"2116_CR32","first-page":"842","volume":"8","author":"M Dyrmann","year":"2017","unstructured":"Dyrmann, M., J\u00f8rgensen, R.N., Midtiby, H.S.: RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network. Adv. Anim. Precis. Agric. 8, 842\u2013847 (2017)","journal-title":"Adv. Anim. Precis. Agric."},{"key":"2116_CR33","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.compind.2018.03.010","volume":"99","author":"PA Dias","year":"2018","unstructured":"Dias, P.A., Tabb, A., Medeiros, H.: Apple flower detection using deep convolutional networks. Comput. Ind. 99, 17\u201328 (2018)","journal-title":"Comput. Ind."},{"issue":"8","key":"2116_CR34","doi-asserted-by":"publisher","first-page":"122","DOI":"10.3390\/s16081222","volume":"16","author":"I Sa","year":"2016","unstructured":"Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., McCool, C.: Deepfruits: a fruit detection system using deep neural networks. Sensors 16(8), 122 (2016)","journal-title":"Sensors"},{"issue":"11","key":"2116_CR35","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.3390\/s16111915","volume":"16","author":"S Madeleine","year":"2016","unstructured":"Madeleine, S., Bargoti, S., Underwood, J.: Image based mango fruit detection, localisation and yield estimation using multiple view geometry. Sensors 16(11), 1915 (2016)","journal-title":"Sensors"},{"key":"2116_CR36","first-page":"1","volume":"11","author":"Y Chen","year":"2019","unstructured":"Chen, Y., Lee, W.S., Gan, H., Peres, N., Fraisse, C., Zhang, Y., He, Y.: Strawberry yield prediction based on a deep neural network using high-resolution aerial orthoimages. Remote Sens 11, 1\u201321 (2019)","journal-title":"Remote Sens"},{"key":"2116_CR37","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.compag.2019.05.016","volume":"162","author":"J Gen\u00e9-Mola","year":"2019","unstructured":"Gen\u00e9-Mola, J., Vilaplana, V., Rosell-Polo, J.R., Morros, J.R., Ruiz-Hidalgo, J., Gregorio, E.: Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities. Comput. Electron. Agric. 162, 689\u2013698 (2019)","journal-title":"Comput. Electron. Agric."},{"key":"2116_CR38","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1007\/s11119-019-09642-0","volume":"20","author":"A Koirala","year":"2019","unstructured":"Koirala, A., Walsh, K.B., Wang, Z., McCarthy, C.: Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of MangoYOLO. Precis. Agric. 20, 1107\u20131135 (2019)","journal-title":"Precis. Agric."},{"key":"2116_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpls.2020.00001","volume":"11","author":"J Liu","year":"2020","unstructured":"Liu, J., Wang, X.: Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network. Front. Plant Sci. 11, 1\u201312 (2020)","journal-title":"Front. Plant Sci."},{"key":"2116_CR40","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.: Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Comput. Electron. Agric. 157, 417\u2013426 (2019)","journal-title":"Comput. Electron. Agric."},{"key":"2116_CR41","first-page":"1","volume":"2019","author":"Y Tian","year":"2019","unstructured":"Tian, Y., Yang, G., Wang, Z., Li, E., Liang, Z.: Detection of apple lesions in orchards based on deep learning methods of CycleGAN and YOLOV3-Dense. J. Sensors. 2019, 1\u201313 (2019)","journal-title":"J. Sensors."},{"issue":"7","key":"2116_CR42","doi-asserted-by":"publisher","first-page":"2145","DOI":"10.3390\/s20072145","volume":"20","author":"G Liu","year":"2020","unstructured":"Liu, G., Nouaze, J.C., Mbouembe, P.L.T., Kim, J.H.: YOLO-tomato: a robust algorithm for tomato detection based on YOLOv3. Sensors 20(7), 2145 (2020). https:\/\/doi.org\/10.3390\/s20072145","journal-title":"Sensors"},{"key":"2116_CR43","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Maaten, L. van der, Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"2116_CR44","unstructured":"Ramachandran, P., Zoph, B., Le, Q. V.: Swish: a self-gated activation function, In: Neural and Evolutionary Computing. pp. 1\u201312 (2017). arXiv:1710.05941"},{"key":"2116_CR45","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Computer Vision\u2014ECCV 2014. Lecture Notes in Computer Science, pp. 740\u2013755 (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2116_CR46","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural networks. In: Advances in Neural Information in Processing Systems. pp. 1135\u20131143 (2015). arXiv:1506.02626v3"},{"key":"2116_CR47","unstructured":"Zhu, P., Wen, L., Du, D., Bian, X., Hu, Q., Ling, H.: Vision meets drones: past, present and future. In: Computer Vision and Pattern Recognition, pp. 1\u201320 (2020). arXiv:2001.06303"},{"issue":"7","key":"2116_CR48","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.: Mixed YOLOv3-LITE: a lightweight real-time object detection method. Sensors 20(7), 1861 (2020)","journal-title":"Sensors"},{"key":"2116_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, P., Zhong, Y., Li, X.: SlimYOLOv3: narrower, faster and better for real-time UAV applications. In: 2019 International Conference on Computer Vision Workshop, pp. 37\u201345 (2019)","DOI":"10.1109\/ICCVW.2019.00011"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02116-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-021-02116-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02116-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T11:08:32Z","timestamp":1654686512000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-021-02116-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,10]]},"references-count":49,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["2116"],"URL":"https:\/\/doi.org\/10.1007\/s00371-021-02116-3","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,10]]},"assertion":[{"value":"22 March 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2021","order":2,"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"}}]}}