{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T07:30:07Z","timestamp":1786087807688,"version":"3.56.0"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T00:00:00Z","timestamp":1560816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Federal Department of Agriculture and Water, through Horticulture Innovation","award":["ST15005"],"award-info":[{"award-number":["ST15005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree (\u201cdual-view\u201d) underestimates the fruit yield as fruit can be hidden from view. A method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting from 10 frame-per-second videos captured of trees from a platform moving along the inter row at 5 km\/h. The deep learning based mango fruit detection algorithm, MangoYOLO, was used to detect fruit in each frame. The Hungarian algorithm was used to correlate fruit between neighbouring frames, with the improvement of enabling multiple-to-one assignment. The Kalman filter was used to predict the position of fruit in following frames, to avoid multiple counts of a single fruit that is obscured or otherwise not detected with a frame series. A \u201cborrow\u201d concept was added to the Kalman filter to predict fruit position when its precise prediction model was absent, by borrowing the horizontal and vertical speed from neighbouring fruit. By comparison with human count for a video with 110 frames and 192 (human count) fruit, the method produced 9.9% double counts and 7.3% missing count errors, resulting in around 2.6% over count. In another test, a video (of 1162 frames, with 42 images centred on the tree trunk) was acquired of both sides of a row of 21 trees, for which the harvest fruit count was 3286 (i.e., average of 156 fruit\/tree). The trees had thick canopies, such that the proportion of fruit hidden from view from any given perspective was high. The proposed method recorded 2050 fruit (62% of harvest) with a bias corrected Root Mean Square Error (RMSE) = 18.0 fruit\/tree while the dual-view image method (also using MangoYOLO) recorded 1322 fruit (40%) with a bias corrected RMSE = 21.7 fruit\/tree. The video tracking system is recommended over the dual-view imaging system for mango orchard fruit count.<\/jats:p>","DOI":"10.3390\/s19122742","type":"journal-article","created":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T02:42:46Z","timestamp":1560912166000},"page":"2742","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":97,"title":["Mango Fruit Load Estimation Using a Video Based MangoYOLO\u2014Kalman Filter\u2014Hungarian Algorithm Method"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5812-2989","authenticated-orcid":false,"given":"Zhenglin","family":"Wang","sequence":"first","affiliation":[{"name":"Centre for Intelligent Systems, Central Queensland University, Rockhampton North 4701, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3033-8622","authenticated-orcid":false,"given":"Kerry","family":"Walsh","sequence":"additional","affiliation":[{"name":"Institute of Future Farming Systems, Central Queensland University, Rockhampton North 4701, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anand","family":"Koirala","sequence":"additional","affiliation":[{"name":"Institute of Future Farming Systems, Central Queensland University, Rockhampton North 4701, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gal\u00e1n Sa\u00faco, V., and Lu, P. (2018). Monitoring fruit quality and quantity in mangoes. Achieving Sustainable Cultivation of Mangoes, Burleigh Dodds Science Publishing Limited.","DOI":"10.4324\/9781351114431"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1002\/9781118060773.ch5","article-title":"Alternate bearing in fruit trees","volume":"4","author":"Monselise","year":"1982","journal-title":"Hortic. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Anderson, N.T., Underwood, J.P., Rahman, M.M., Robson, A., and Walsh, K.B. (2018). Estimation of fruit load in mango orchards: Tree sampling considerations and use of machine vision and satellite imagery. Precis. Agric.","DOI":"10.1007\/s11119-018-9614-1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.13031\/2013.25302","article-title":"Estimation of Wild Blueberry Fruit Yield Using Digital Color Photography","volume":"51","author":"Zaman","year":"2008","journal-title":"Trans. Asabe"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1007\/s11119-012-9269-2","article-title":"Using colour features of cv. \u2018Gala\u2019 apple fruits in an orchard in image processing to predict yield","volume":"13","author":"Zhou","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_6","unstructured":"Annamalai, P., and Lee, W.S. (2003, January 27\u201330). Citrus Yield Mapping System Using Machine Vision. Proceedings of the Annual International Conference of The American Society of Agricultural Engineers, Las Vegas, NV, USA."},{"key":"ref_7","unstructured":"Fischler, M.A., and Firschein, O. (1987). A Computational Approach to Edge Detection. Readings in Computer Vision, Morgan Kaufmann."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jianbo, S., and Tomasi, C. (1994, January 21\u201323). Good features to track. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR.1994.323794"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/BF01469346","article-title":"Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention","volume":"11","author":"Lindeberg","year":"1993","journal-title":"Int. J. Comput. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1023\/A:1008379107611","article-title":"Properties of ridges and cores for two-dimensional images","volume":"10","author":"Damon","year":"1999","journal-title":"J. Math. Imaging Vis."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201327). Object recognition from local scale-invariant features. Proceedings of the 7th IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006). Surf: Speeded up robust features. European Conference on Computer Vision, Springer.","DOI":"10.1007\/11744023_32"},{"key":"ref_13","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ahonen, T., Hadid, A., and Pietik\u00e4inen, M. (2004). Face recognition with local binary patterns. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-540-24670-1_36"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.compag.2011.07.001","article-title":"Green citrus detection using \u2018eigenfruit\u2019, color and circular Gabor texture features under natural outdoor conditions","volume":"78","author":"Kurtulmus","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.compag.2011.11.007","article-title":"Determination of the number of green apples in RGB images recorded in orchards","volume":"81","author":"Linker","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1007\/s11119-016-9458-5","article-title":"Machine vision for counting fruit on mango tree canopies","volume":"18","author":"Qureshi","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_18","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014). Visualizing and understanding convolutional networks. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2019.04.017","article-title":"Deep learning\u2014Method overview and review of use for fruit detection and yield estimation","volume":"162","author":"Koirala","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., and McCool, C. (2016). DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors, 16.","DOI":"10.3390\/s16081222"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2016). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, The MIT Press.","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/LRA.2017.2651944","article-title":"Counting Apples and Oranges with Deep Learning: A Data-Driven Approach","volume":"2","author":"Chen","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bargoti, S., and Underwood, J. (June, January 29). Deep fruit detection in orchards. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989417"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.compag.2012.11.009","article-title":"Estimation of mango crop yield using image analysis-Segmentation method","volume":"91","author":"Payne","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.compag.2013.11.011","article-title":"Estimating mango crop yield using image analysis using fruit at \u2018stone hardening\u2019 stage and night time imaging","volume":"100","author":"Payne","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Stein, M., Bargoti, S., and Underwood, J. (2016). Image based mango fruit detection, localisation and yield estimation using multiple view geometry. Sensors, 16.","DOI":"10.3390\/s16111915"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Robson, A., Rahman, M., and Muir, J. (2017). Using worldview satellite imagery to map yield in avocado (Persea americana): A case study in Bundaberg, Australia. Remote Sens., 9.","DOI":"10.3390\/rs9121223"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Koirala, A., Walsh, K.B., Wang, Z., and McCarthy, C. (2019). Deep learning for real-time fruit detection and orchard fruit load estimation: Benchmarking of \u2018MangoYOLO\u2019. Precis. Agric., 1\u201329.","DOI":"10.1007\/s11119-019-09642-0"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.compag.2018.06.040","article-title":"Machine vision assessment of mango orchard flowering","volume":"151","author":"Wang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.1109\/LRA.2019.2901987","article-title":"Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association","volume":"4","author":"Liu","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-Speed Tracking with Kernelized Correlation Filters","volume":"37","author":"Henriques","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","article-title":"Determining optical flow","volume":"17","author":"Horn","year":"1981","journal-title":"Artif. Intell."},{"key":"ref_39","unstructured":"Zarchan, P., and Musoff, H. (2013). Fundamentals of Kalman Filtering: A Practical Approach, American Institute of Aeronautics and Astronautics, Inc."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Moonrinta, J., Chaivivatrakul, S., Dailey, M.N., and Ekpanyapong, M. (2010, January 7\u201310). Fruit detection, tracking, and 3D reconstruction for crop mapping and yield estimation. Proceedings of the 2010 11th International Conference on Control Automation Robotics & Vision, Singapore.","DOI":"10.1109\/ICARCV.2010.5707436"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Desai, P.J., Dudek, G., Khatib, O., and Kumar, V. (2013). Automated Crop Yield Estimation for Apple Orchards. Experimental Robotics, Proceedings of the 13th International Symposium on Experimental Robotics, Qu\u00e9bec City, QC, Canada, 18\u201321 June 2012, Springer International Publishing.","DOI":"10.1007\/978-3-319-00065-7"},{"key":"ref_42","unstructured":"Gan, H., Lee, W.S., and Alchanatis, V. (2017, January 16\u201319). A Prototype of an Immature Citrus Fruit Yield Mapping System. Proceedings of the 2017 ASABE Annual International Meeting, Spokane, WA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2995","DOI":"10.1109\/LRA.2018.2849514","article-title":"Fruit Quantity and Ripeness Estimation Using a Robotic Vision System","volume":"3","author":"Halstead","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_44","unstructured":"Underwood, J., Rahman, M., Robson, A., Walsh, K., Koirala, A., and Wang, Z. (2018, January 21\u201325). Fruit load estimation in mango orchards\u2014A method comparison. Proceedings of the ICRA 2018 Workshop on Robotic Vision and Action in Agriculture, Brisbane, Australia."},{"key":"ref_45","first-page":"23","article-title":"A threshold selection method from gray-level histograms","volume":"11","author":"Otsu","year":"1975","journal-title":"Automatica"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.scienta.2016.11.041","article-title":"Can the productivity of mango orchards be increased by using high-density plantings?","volume":"219","author":"Menzel","year":"2017","journal-title":"Sci. Hortic."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2742\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:59:25Z","timestamp":1760187565000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2742"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,18]]},"references-count":46,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["s19122742"],"URL":"https:\/\/doi.org\/10.3390\/s19122742","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,18]]}}}