{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T17:24:46Z","timestamp":1767374686641,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031477232"},{"type":"electronic","value":"9783031477249"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-47724-9_41","type":"book-chapter","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T20:29:08Z","timestamp":1713472148000},"page":"629-640","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Kiwifruit Counting Using Kiwidetector and Kiwitracker"],"prefix":"10.1007","author":[{"given":"Yi","family":"Xia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei Qi","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,19]]},"reference":[{"key":"41_CR1","unstructured":"An, N., Yan, W.Q.: Anomalies detection and tracking using Siamese neural networks. Master\u2019s Thesis, Auckland University of Technology, New Zealand. (2020)"},{"key":"41_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3441656","volume":"17","author":"N An","year":"2021","unstructured":"An, N., Yan, W.Q.: Multitarget tracking using Siamese neural networks. ACM Trans. Multimed. Comput. Commun. Appl. 17, 1\u201316 (2021)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"41_CR3","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP) (2016)","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"41_CR4","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: optimal speed and accuracy of object detection. https:\/\/arxiv.org\/abs\/2004.10934"},{"key":"41_CR5","unstructured":"Fu, Y.H., Yan, W.Q.: Fruit freshness grading using deep learning. Master\u2019s Thesis, Auckland University of Technology, New Zealand (2020)"},{"key":"41_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107000","volume":"197","author":"F Gao","year":"2022","unstructured":"Gao, F., et al.: A novel apple fruit detection and counting methodology based on deep learning and trunk tracking in modern Orchard. Comput. Electron. Agric. 197, 107000 (2022)","journal-title":"Comput. Electron. Agric."},{"key":"41_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105634","volume":"176","author":"F Gao","year":"2020","unstructured":"Gao, F., et al.: Multi-class fruit-on-plant detection for Apple in SNAP system using faster R-CNN. Comput. Electron. Agric. 176, 105634 (2020)","journal-title":"Comput. Electron. Agric."},{"key":"41_CR8","doi-asserted-by":"publisher","DOI":"10.1117\/1.OE.56.6.063102","volume":"56","author":"Q Gu","year":"2017","unstructured":"Gu, Q., Yang, J., Kong, L., Yan, W.Q., Klette, R.: Embedded and real-time vehicle detection system for challenging on-road scenes. Opt. Eng. 56, 063102 (2017)","journal-title":"Opt. Eng."},{"key":"41_CR9","doi-asserted-by":"publisher","first-page":"6006","DOI":"10.3390\/app11136006","volume":"11","author":"H Le","year":"2021","unstructured":"Le, H., Nguyen, M., Yan, W.Q., Nguyen, H.: Augmented reality and machine learning incorporation using yolov3 and Arkit. Appl. Sci. 11, 6006 (2021)","journal-title":"Appl. Sci."},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Liu, W., Li, Y., Tomasetto, F., Yan, W., Tan, Z., Liu, J., Jiang, J.: Non-destructive measurements of Toona sinensis chlorophyll and nitrogen content under drought stress using near infrared spectroscopy. Front. Plant Sci. 12 (2022)","DOI":"10.3389\/fpls.2021.809828"},{"key":"41_CR11","doi-asserted-by":"publisher","first-page":"6715","DOI":"10.3233\/JIFS-210597","volume":"41","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Yang, G., Huang, Y., Yin, Y.: Se-mask R-CNN: an improved mask R-CNN for Apple Detection and segmentation. J. Intell. Fuzzy Syst. 41, 6715\u20136725 (2021)","journal-title":"J. Intell. Fuzzy Syst."},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"Liu, X., Yan, W.Q.: Vehicle-related distance estimation using customized Yolov7. Image Vision Comput. 91\u2013103 (2023)","DOI":"10.1007\/978-3-031-25825-1_7"},{"key":"41_CR13","doi-asserted-by":"crossref","unstructured":"Luo, Z., Yan, W.Q., Nguyen, M.: Kayak and sailboat detection based on the improved Yolo with Transformer. In: 2022 The 5th International Conference on Control and Computer Vision (2022)","DOI":"10.1145\/3561613.3561619"},{"key":"41_CR14","unstructured":"Luo, Z., Yan, W.Q., Nguyen, M.: Sailboat and kayak detection using deep learning methods. Master\u2019s Thesis, Auckland University of Technology, New Zealand (2022)"},{"key":"41_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.scienta.2019.108615","volume":"256","author":"J Lv","year":"2019","unstructured":"Lv, J., Ni, H., Wang, Q., Yang, B., Xu, L.: A segmentation method of Red Apple Image. Sci. Hortic. 256, 108615 (2019)","journal-title":"Sci. Hortic."},{"key":"41_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106132","volume":"185","author":"J Massah","year":"2021","unstructured":"Massah, J., Asefpour Vakilian, K., Shabanian, M., Shariatmadari, S.M.: Design, development, and performance evaluation of a robot for yield estimation of Kiwifruit. Comput. Electron. Agric. 185, 106132 (2021)","journal-title":"Comput. Electron. Agric."},{"key":"41_CR17","doi-asserted-by":"publisher","first-page":"4773","DOI":"10.1109\/TIP.2021.3074796","volume":"30","author":"C Pan","year":"2021","unstructured":"Pan, C., Liu, J., Yan, W.Q., Cao, F., He, W., Zhou, Y.: Salient object detection based on visual perceptual saturation and two-stream hybrid networks. IEEE Trans. Image Process. 30, 4773\u20134787 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"41_CR18","doi-asserted-by":"publisher","first-page":"19925","DOI":"10.1007\/s11042-020-08866-x","volume":"79","author":"C Pan","year":"2020","unstructured":"Pan, C., Yan, W.Q.: Object detection based on saturation of Visual perception. Multimedia Tools Appl. 79, 19925\u201319944 (2020)","journal-title":"Multimedia Tools Appl."},{"key":"41_CR19","doi-asserted-by":"crossref","unstructured":"Qi, J., Nguyen, M., Yan, W.Q.: Small visual object detection in smart waste classification using Transformers with deep learning. Image and Vision Comput. 301\u2013314 (2023)","DOI":"10.1007\/978-3-031-25825-1_22"},{"key":"41_CR20","doi-asserted-by":"publisher","first-page":"905","DOI":"10.3390\/s17040905","volume":"17","author":"M Rahnemoonfar","year":"2017","unstructured":"Rahnemoonfar, M., Sheppard, C.: Deep count: Fruit counting based on deep simulated learning. Sensors. 17, 905 (2017)","journal-title":"Sensors."},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"41_CR22","doi-asserted-by":"crossref","unstructured":"Shen, D., Chen, X., Nguyen, M., Yan, W.Q.: Flame detection using Deep Learning. In: 2018 4th International Conference on Control, Automation and Robotics (ICCAR) (2018)","DOI":"10.1109\/ICCAR.2018.8384711"},{"key":"41_CR23","doi-asserted-by":"crossref","unstructured":"Song, Z., Tomasetto, F., Niu, X., Yan, W.Q., Jiang, J., Li, Y.: Enabling breeding selection for biomass in slash pine using UAV-based imaging. Plant Phenomics (2022)","DOI":"10.34133\/2022\/9783785"},{"key":"41_CR24","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. https:\/\/arxiv.org\/abs\/2207.02696"},{"key":"41_CR25","doi-asserted-by":"crossref","unstructured":"Wang, D., He, D.: Apple Detection and instance segmentation in natural environments using an improved mask scoring R-CNN model. Front. Plant Sci. 13 (2022)","DOI":"10.3389\/fpls.2022.1016470"},{"key":"41_CR26","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a Deep Association metric. In: 2017 IEEE International Conference on Image Processing (ICIP) (2017)","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"41_CR27","doi-asserted-by":"crossref","unstructured":"Xiao, B., Nguyen, M., Yan, W.Q.: Apple ripeness identification using Deep Learning. Commun. Comput. Inf. Sci. 53\u201367 (2021)","DOI":"10.1007\/978-3-030-72073-5_5"},{"key":"41_CR28","doi-asserted-by":"crossref","unstructured":"Xia, Y., Nguyen, M., Yan, W.Q.: A real-time kiwifruit detection based on improved Yolov7. Image and Vision Comput. 48\u201361 (2023)","DOI":"10.1007\/978-3-031-25825-1_4"},{"key":"41_CR29","unstructured":"Xin, C., Nguyen, M., Yan, W.Q.: Detection and recognition for multiple flames using deep learning. Master\u2019s Thesis, Auckland University of Technology, New Zealand (2018)"},{"key":"41_CR30","doi-asserted-by":"crossref","unstructured":"Xin, C., Nguyen, M., Yan, W.Q.: Multiple flames recognition using Deep Learning. Handbook of Research on Multimedia Cyber Security 296\u2013307 (2020)","DOI":"10.4018\/978-1-7998-2701-6.ch015"},{"key":"41_CR31","unstructured":"Xing, J.W., Yan, W.Q.: Traffic sign recognition from digital images by using deep learning. Master\u2019s Thesis, Auckland University of Technology, New Zealand (2020)"},{"key":"41_CR32","doi-asserted-by":"crossref","unstructured":"Yan, W.Q.: Computational methods for deep learning: Theoretic, practice and applications. Springer (2021)","DOI":"10.1007\/978-3-030-61081-4"},{"key":"41_CR33","doi-asserted-by":"crossref","unstructured":"Yan, W.Q.: Introduction to intelligent surveillance. Springer International Publishing (2019)","DOI":"10.1007\/978-3-030-10713-0_1"},{"key":"41_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Yan, W.Q.: Currency detection and recognition based on Deep Learning. In: 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (2018)","DOI":"10.1109\/AVSS.2018.8639124"},{"key":"41_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Shen, K.J., He, Z.F., Pan, Z.S.: Yolo-infrared: Enhancing Yolox for infrared scene. J. Phys. Conf. Ser. 2405, 012015","DOI":"10.1088\/1742-6596\/2405\/1\/012015"},{"key":"41_CR36","doi-asserted-by":"crossref","unstructured":"Zhao, K., Yan, W.Q.: Fruit detection from digital images using CenterNet. Commun. Comput. Inf. Sci. 313\u2013326 (2021)","DOI":"10.1007\/978-3-030-72073-5_24"},{"key":"41_CR37","unstructured":"Zhao, K., Yan, W.Q.: Fruit detection using CenterNet. Master\u2019s Thesis, Auckland University of Technology, New Zealand (2021)"},{"key":"41_CR38","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Yan, W.Q.: Parasite detection from digital images using Deep Learning. Machine Learning and AI Techniques in Interactive Medical Image Analysis, pp. 124\u2013134 (2022)","DOI":"10.4018\/978-1-6684-4671-3.ch007"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47724-9_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T20:39:54Z","timestamp":1713472794000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47724-9_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031477232","9783031477249"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47724-9_41","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"19 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}