{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T01:18:25Z","timestamp":1790644705704,"version":"4.1.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Modern people who value healthy eating habits have shown increasing interest in plum (<jats:italic>Prunus mume<\/jats:italic>) fruits, primarily owing to their nutritiousness and proven efficacy. As consumption increases, it becomes important to monitor work to prevent <jats:italic>Prunus mume<\/jats:italic> fruits from falling out. Moreover, determining the growth status of <jats:italic>Prunus mume<\/jats:italic> is also crucial and is attracting increasing attention. In this study, convolutional neural network (CNN)-based deep learning object detection was developed using RGBD images collected from <jats:italic>Prunus mume<\/jats:italic> farms. These RGBD images consider various environments, including the depth information of objects in the outdoor field. A faster region-based convolutional neural network (R-CNN), EfficientDet, Retinanet, and Single Shot Multibox Detector (SSD) were applied for detection, and the performance of all models was estimated by comparing their respective computing speeds and average precisions (APs). The test results show that the EfficientDet model is the most accurate, and SSD MobileNet is the fastest among the four models. In addition, the algorithm was developed to acquire the growth status of <jats:italic>P. mume<\/jats:italic> fruits by applying the coordinates and score values of bounding boxes to the depth map. Compared to the diameters of the artificial <jats:italic>Prunus mume<\/jats:italic> fruits used as the experimental group, the calculated diameters were very similar to those of the artificial objects. Collectively, the results demonstrate that the CNN-based deep learning <jats:italic>Prunus mume<\/jats:italic> detection and growth estimation method can be applied to real farmlands.<\/jats:p>","DOI":"10.1038\/s41598-022-25260-9","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T12:00:50Z","timestamp":1669982450000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["CNN-based object detection and growth estimation of plum fruit (Prunus mume) using RGB and depth imaging techniques"],"prefix":"10.1038","volume":"12","author":[{"given":"EungChan","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suk-Ju","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang-Yeon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang-Hyup","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungjay","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyuck-Joo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghiseok","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,2]]},"reference":[{"key":"25260_CR1","doi-asserted-by":"crossref","unstructured":"Moghadam, P. et al. Plant disease detection using hyperspectral imaging. In DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications vols. 2017-December 1\u20138 (Institute of Electrical and Electronics Engineers Inc., 2017).","DOI":"10.1109\/DICTA.2017.8227476"},{"key":"25260_CR2","doi-asserted-by":"publisher","unstructured":"Chetan Dwarkani, M., Ganesh Ram, R., Jagannathan, S., Priyatharshini, R. Smart farming system using sensors for agricultural task automation. In Proceedings - 2015 IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development, TIAR 2015 49\u201353 (Institute of Electrical and Electronics Engineers Inc., 2015). Doi: https:\/\/doi.org\/10.1109\/TIAR.2015.7358530.","DOI":"10.1109\/TIAR.2015.7358530"},{"key":"25260_CR3","doi-asserted-by":"publisher","unstructured":"Migdall, S., Klug, P., Denis, A., Bach, H. The additional value of hyperspectral data for smart farming. In International Geoscience and Remote Sensing Symposium (IGARSS) 7329\u20137332 (2012). Doi:https:\/\/doi.org\/10.1109\/IGARSS.2012.6351937.","DOI":"10.1109\/IGARSS.2012.6351937"},{"key":"25260_CR4","first-page":"114","volume":"26","author":"MH Meyer","year":"2016","unstructured":"Meyer, M. H. et al. Importance of horticulture and perception as a career. J. Am. Soc. Horticu. Sci. 26, 114 (2016).","journal-title":"J. Am. Soc. Horticu. Sci."},{"key":"25260_CR5","first-page":"37","volume":"1","author":"T Grift","year":"2008","unstructured":"Grift, T., Zhang, Q., Kondo, N. & Ting, K. C. A review of automation and robotics for the bio-industry. J. Biomech. Eng. 1, 37 (2008).","journal-title":"J. Biomech. Eng."},{"key":"25260_CR6","doi-asserted-by":"publisher","first-page":"388","DOI":"10.21273\/HORTTECH.22.3.388","volume":"22","author":"B Posadas","year":"2012","unstructured":"Posadas, B. Economic impacts of mechanization or automation on horticulture production firms sales, employment, and workers\u2019 earnings, safety, and retention. HortTechnology 22, 388\u2013401 (2012).","journal-title":"HortTechnology"},{"key":"25260_CR7","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1016\/S1360-1385(00)01781-7","volume":"5","author":"L Chaerle","year":"2000","unstructured":"Chaerle, L. & van der Straeten, D. Imaging techniques and the early detection of plant stress. Trends Plant Sci. 5, 495\u2013501 (2000).","journal-title":"Trends Plant Sci."},{"key":"25260_CR8","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.rse.2015.04.032","volume":"165","author":"L Liang","year":"2015","unstructured":"Liang, L. et al. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sens. Environ. 165, 123\u2013134 (2015).","journal-title":"Remote Sens. Environ."},{"key":"25260_CR9","doi-asserted-by":"publisher","first-page":"34","DOI":"10.12791\/KSBEC.2018.27.1.34","volume":"27","author":"S-H Kim","year":"2018","unstructured":"Kim, S.-H. et al. Estimation of moisture content in cucumber and watermelon seedlings using hyperspectral imagery. Prot. Hortic. Plant Factory 27, 34\u201339 (2018).","journal-title":"Prot. Hortic. Plant Factory"},{"key":"25260_CR10","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s42853-019-00014-0","volume":"44","author":"JK Basak","year":"2019","unstructured":"Basak, J. K. et al. Regression analysis to estimate morphology parameters of pepper plant in a controlled greenhouse system. J. Biosyst. Eng. 44, 57\u201368 (2019).","journal-title":"J. Biosyst. Eng."},{"key":"25260_CR11","first-page":"1","volume":"1","author":"P Birwal","year":"2017","unstructured":"Birwal, P., Deshmukh, G. & Saurabh, S. P. Nutrition and population health citation. Rev. Art. J. Food 1, 1 (2017).","journal-title":"Rev. Art. J. Food"},{"key":"25260_CR12","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1002\/ptr.5581","volume":"30","author":"EO Igwe","year":"2016","unstructured":"Igwe, E. O. & Charlton, K. E. A Systematic review on the health effects of plums (Prunus domestica and Prunus salicina). Phytother. Res. 30, 701\u2013731 (2016).","journal-title":"Phytother. Res."},{"key":"25260_CR13","doi-asserted-by":"publisher","first-page":"496","DOI":"10.3390\/nu9050496","volume":"9","author":"BH Arjmandi","year":"2017","unstructured":"Arjmandi, B. H. et al. Bone-protective effects of dried plum in postmenopausal women: Efficacy and possible mechanisms. Nutrients 9, 496 (2017).","journal-title":"Nutrients"},{"key":"25260_CR14","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/S2221-1691(14)60216-1","volume":"4","author":"HR Kim","year":"2014","unstructured":"Kim, H. R., Kim, I. D., Dhungana, S. K., Kim, M. O. & Shin, D. H. Comparative assessment of physicochemical properties of unripe peach (Prunus persica) and Japanese apricot (Prunus mume). Asian Pac. J. Trop. Biomed. 4, 97\u2013103 (2014).","journal-title":"Asian Pac. J. Trop. Biomed."},{"key":"25260_CR15","doi-asserted-by":"publisher","first-page":"C388","DOI":"10.1111\/j.1750-3841.2012.02624.x","volume":"77","author":"Y Wang","year":"2012","unstructured":"Wang, Y., Chen, X., Zhang, Y. & Chen, X. Antioxidant activities and major anthocyanins of myrobalan plum (Prunus cerasifera Ehrh.). J Food Sci 77, C388\u2013C393 (2012).","journal-title":"J Food Sci"},{"key":"25260_CR16","doi-asserted-by":"publisher","first-page":"334","DOI":"10.21273\/JASHS.132.3.334","volume":"132","author":"M Vizzotto","year":"2007","unstructured":"Vizzotto, M., Cisneros-Zevallos, L., Byrne, D. H., Ramming, D. W. & Okie, W. R. Large variation found in the phytochemical and antioxidant activity of peach and plum germplasm. J. Am. Soc. Hortic. Sci. 132, 334\u2013340 (2007).","journal-title":"J. Am. Soc. Hortic. Sci."},{"key":"25260_CR17","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1292\/jvms.09-0555","volume":"72","author":"B-G Jung","year":"2010","unstructured":"Jung, B.-G. et al. Immune-enhancing effect of fermented maesil (Prunus mume Siebold & Zucc.) with probiotics against bordetella bronchiseptica in mice. J. Vet. Med. Sci. 72, 1195\u20131202 (2010).","journal-title":"J. Vet. Med. Sci."},{"key":"25260_CR18","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1080\/10942910600547624","volume":"10","author":"M Adachi","year":"2007","unstructured":"Adachi, M. et al. The \u201cPrunus mume Sieb et. Zucc\u201d (Ume) is a rich natural source of novel anti-cancer substance. Int. J. Food Prop. 10, 375\u2013384 (2007).","journal-title":"Int. J. Food Prop."},{"key":"25260_CR19","doi-asserted-by":"publisher","first-page":"11432","DOI":"10.1021\/acs.jafc.8b03686","volume":"66","author":"MR Go","year":"2018","unstructured":"Go, M. R., Kim, H. J., Yu, J. & Choi, S. J. Toxicity and toxicokinetics of amygdalin in maesil (Prunus mume) syrup: Protective effect of maesil against amygdalin toxicity. J. Agric. Food Chem. 66, 11432\u201311440 (2018).","journal-title":"J. Agric. Food Chem."},{"key":"25260_CR20","first-page":"706","volume":"13","author":"T Milo\u0161evi\u0107","year":"2013","unstructured":"Milo\u0161evi\u0107, T., Milo\u0161evi\u0107, N. & Gli\u0161i\u0107, I. Agronomic properties and nutritional status of plum trees (Prunus domestica L.) influenced by different cultivars. J. Soil Sci. Plant Nutr. 13, 706\u2013714 (2013).","journal-title":"J. Soil Sci. Plant Nutr."},{"key":"25260_CR21","doi-asserted-by":"publisher","first-page":"5569","DOI":"10.3390\/s20195569","volume":"20","author":"J Ahmad","year":"2020","unstructured":"Ahmad, J., Jan, B., Farman, H., Ahmad, W. & Ullah, A. Disease detection in plum using convolutional neural network under true field conditions. Sensors 20, 5569 (2020).","journal-title":"Sensors"},{"key":"25260_CR22","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.5656\/KSAE.2015.06.0.016","volume":"54","author":"DS Choi","year":"2015","unstructured":"Choi, D. S. et al. Damage, occurrence, and optimal control period of Eurytoma maslovskii affecting Japanese Apricot (Prunus mume) fruits in Jeonnam province. Korean J. Appl. Entomol. 54, 2287\u20132545 (2015).","journal-title":"Korean J. Appl. Entomol."},{"key":"25260_CR23","doi-asserted-by":"publisher","first-page":"184","DOI":"10.5338\/KJEA.2016.35.3.25","volume":"21","author":"JD Lee","year":"2016","unstructured":"Lee, J. D., Kim, G. S., Kong, H. J., Cho, S. Y., Kim, H. J. Development of growth and cultivation environment monitoring system to determine the right time to control plums. J. Korean Soc. Agric. Mach.21, 184\u2013184 (2016).","journal-title":"J. Korean Soc. Agric. Mach."},{"key":"25260_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. et al. 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":"25260_CR25","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/s42853-022-00136-y","volume":"47","author":"BTW Putra","year":"2022","unstructured":"Putra, B. T. W., Amirudin, R. & Marhaenanto, B. The evaluation of deep learning using convolutional neural network (CNN) approach for identifying Arabica and Robusta coffee plants. J. Biosyst. Eng. 47, 118\u2013129. https:\/\/doi.org\/10.1007\/s42853-022-00136-y (2022).","journal-title":"J. Biosyst. Eng."},{"key":"25260_CR26","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84\u201390 (2017).","journal-title":"Commun. ACM"},{"key":"25260_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05064-6","author":"S Rasti","year":"2020","unstructured":"Rasti, S. et al. Crop growth stage estimation prior to canopy closure using deep learning algorithms. Neural Comput. Appl. https:\/\/doi.org\/10.1007\/s00521-020-05064-6 (2020).","journal-title":"Neural Comput. Appl."},{"key":"25260_CR28","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.3390\/s18051580","volume":"18","author":"N Teimouri","year":"2018","unstructured":"Teimouri, N. et al. Weed growth stage estimator using deep convolutional neural networks. Sensors 18, 1580 (2018).","journal-title":"Sensors"},{"key":"25260_CR29","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1016\/j.cell.2020.01.021","volume":"180","author":"JM Stokes","year":"2020","unstructured":"Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180, 688-702.e13 (2020).","journal-title":"Cell"},{"key":"25260_CR30","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.ins.2016.01.082","volume":"345","author":"MMA Rahhal","year":"2016","unstructured":"Rahhal, M. M. A. et al. Deep learning approach for active classification of electrocardiogram signals. Inf. Sci. 345, 340\u2013354 (2016).","journal-title":"Inf. Sci."},{"key":"25260_CR31","unstructured":"Paulo Marques do Nascimento, P. Applications of deep learning techniques on NILM. Diss. Universidade Federal do Rio de Janeiro (2016)."},{"key":"25260_CR32","doi-asserted-by":"publisher","first-page":"312","DOI":"10.3390\/rs9040312","volume":"9","author":"N Ammour","year":"2017","unstructured":"Ammour, N. et al. Deep learning approach for car detection in UAV imagery. Remote Sens. 9, 312 (2017).","journal-title":"Remote Sens."},{"key":"25260_CR33","doi-asserted-by":"publisher","first-page":"142","DOI":"10.3390\/agronomy9030142","volume":"9","author":"C-H Chen","year":"2019","unstructured":"Chen, C.-H., Kung, H.-Y. & Hwang, F.-J. Deep learning techniques for agronomy applications. Agronomy 9, 142 (2019).","journal-title":"Agronomy"},{"key":"25260_CR34","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.3390\/s19071651","volume":"19","author":"SJ Hong","year":"2019","unstructured":"Hong, S. J., Han, Y., Kim, S. Y., Lee, A. Y. & Kim, G. Application of deep-learning methods to bird detection using unmanned aerial vehicle imagery. Sensors 19, 1651 (2019).","journal-title":"Sensors"},{"key":"25260_CR35","unstructured":"Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (International Conference on Learning Representations, ICLR, 2015)."},{"key":"25260_CR36","first-page":"2289","volume":"17","author":"Y-D Joo","year":"2017","unstructured":"Joo, Y.-D. Drone image classification based on convolutional neural networks. J. Inst. Internet, Broadcast. Commun. 17, 2289 (2017).","journal-title":"J. Inst. Internet, Broadcast. Commun."},{"key":"25260_CR37","doi-asserted-by":"crossref","unstructured":"Bharati, P., Pramanik, A. Deep learning techniques\u2014R-CNN to Mask R-CNN: A survey. Advances in Intelligent Systems and Computing 999, 657\u2013668 (Springer, 2020)","DOI":"10.1007\/978-981-13-9042-5_56"},{"key":"25260_CR38","doi-asserted-by":"crossref","unstructured":"Girshick, R. Fast R-CNN. In Proceedings of the IEEE Conference on Computer Vision (ICCV), 1440\u20131448 (2015).","DOI":"10.1109\/ICCV.2015.169"},{"key":"25260_CR39","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137\u20131149 (2017).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25260_CR40","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P. & Girshick, R. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961\u20132969 (2017).","DOI":"10.1109\/ICCV.2017.322"},{"key":"25260_CR41","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s42853-020-00063-w","volume":"45","author":"HS Lee","year":"2020","unstructured":"Lee, H. S. & Shin, B. S. Potato detection and segmentation based on mask R-CNN. J. Biosyst. Eng. 45, 233\u2013238. https:\/\/doi.org\/10.1007\/s42853-020-00063-w (2020).","journal-title":"J. Biosyst. Eng."},{"key":"25260_CR42","doi-asserted-by":"crossref","unstructured":"Liu, W. et al. SSD: Single shot multibox detector. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 9905 LNCS 21\u201337 (Springer Verlag, 2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"25260_CR43","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K. & Doll\u00e1r, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, 2980\u20132988 (2017).","DOI":"10.1109\/ICCV.2017.324"},{"key":"25260_CR44","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R. & Le, Q. v. EfficientDet: Scalable and efficient object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10781\u201310790 (2020).","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"25260_CR45","first-page":"130","volume":"21","author":"E-C Jang","year":"2021","unstructured":"Jang, E.-C. et al. 3D image processing for recognition and size estimation of the fruit of plum (Japanese apricot). J. Korea Contents Assoc. 21, 130\u2013139 (2021).","journal-title":"J. Korea Contents Assoc."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-25260-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-25260-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-25260-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T12:03:54Z","timestamp":1669982634000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-25260-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,2]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["25260"],"URL":"https:\/\/doi.org\/10.1038\/s41598-022-25260-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,2]]},"assertion":[{"value":"18 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"20796"}}