{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:46:31Z","timestamp":1760060791815,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AgRibot-Harnessing Robotics, XR\/AR, and 5G for a New Era of Safe, Sustainable, and Smart Agriculture, European Union\u2019s Horizon Europe research and innovation programme","award":["101183158","DIT.AD022.207","FOE 2022"],"award-info":[{"award-number":["101183158","DIT.AD022.207","FOE 2022"]}]},{"name":"CNR DIITET","award":["101183158","DIT.AD022.207","FOE 2022"],"award-info":[{"award-number":["101183158","DIT.AD022.207","FOE 2022"]}]},{"name":"STRIVE-le Scienze per le TRansizioni Industriale, Verde ed Energetica","award":["101183158","DIT.AD022.207","FOE 2022"],"award-info":[{"award-number":["101183158","DIT.AD022.207","FOE 2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Pomegranate (Punica granatum) fruit size estimation plays a crucial role in orchard management decision-making, especially for fruit quality assessment and yield prediction. Currently, fruit sizing for pomegranates is performed manually using calipers to measure equatorial and polar diameters. These methods rely on human judgment for sample selection, they are labor-intensive, and prone to errors. In this work, a novel framework for automated on-tree detection and sizing of pomegranate fruits by a farmer robot equipped with a consumer-grade RGB-D sensing device is presented. The proposed system features a multi-stage transfer learning approach to segment fruits in RGB images. Segmentation results from each image are projected on the co-located depth image; then, a fruit clustering and modeling algorithm using visual and depth information is implemented for fruit size estimation. Field tests carried out in a commercial orchard are presented for 96 pomegranate fruit samples, showing that the proposed approach allows for accurate fruit size estimation with an average discrepancy with respect to caliper measures of about 1.0 cm on both the polar and equatorial diameter.<\/jats:p>","DOI":"10.3390\/robotics14100131","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T14:32:53Z","timestamp":1758637973000},"page":"131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated On-Tree Detection and Size Estimation of Pomegranates by a Farmer Robot"],"prefix":"10.3390","volume":"14","author":[{"given":"Rosa Pia","family":"Devanna","sequence":"first","affiliation":[{"name":"Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), Via G. Amendola 122 D-O, 70126 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Vicino","sequence":"additional","affiliation":[{"name":"Department of Soil, Plant and Food Science (DiSSPA), University of Bari, Via G. Amendola 165\/A, 70126 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2825-2090","authenticated-orcid":false,"given":"Simone Pietro","family":"Garofalo","sequence":"additional","affiliation":[{"name":"Department of Soil, Plant and Food Science (DiSSPA), University of Bari, Via G. Amendola 165\/A, 70126 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5798-2573","authenticated-orcid":false,"given":"Gaetano Alessandro","family":"Vivaldi","sequence":"additional","affiliation":[{"name":"Department of Soil, Plant and Food Science (DiSSPA), University of Bari, Via G. Amendola 165\/A, 70126 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6699-3485","authenticated-orcid":false,"given":"Simone","family":"Pascuzzi","sequence":"additional","affiliation":[{"name":"Department of Soil, Plant and Food Science (DiSSPA), University of Bari, Via G. Amendola 165\/A, 70126 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1793-4419","authenticated-orcid":false,"given":"Giulio","family":"Reina","sequence":"additional","affiliation":[{"name":"Department of Mechanics, Mathematics and Management, Polytechnic of Bari, Via Orabona 4, 70125 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9456-7590","authenticated-orcid":false,"given":"Annalisa","family":"Milella","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), Via G. Amendola 122 D-O, 70126 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Montefusco, A., Durante, M., Migoni, D., De Caroli, M., Ilahy, R., P\u00e9k, Z., Helyes, L., Fanizzi, F.P., Mita, G., and Piro, G. (2021). Analysis of the Phytochemical Composition of Pomegranate Fruit Juices, Peels and Kernels: A Comparative Study on Four Cultivars Grown in Southern Italy. Plants, 10.","DOI":"10.3390\/plants10112521"},{"key":"ref_2","unstructured":"(2025, September 09). Available online: https:\/\/esploradati.istat.it\/databrowser\/#\/it\/dw\/categories\/IT1,Z1000AGR,1.0\/AGR_CRP\/DCSP_COLTIVAZIONI\/IT1,101_1015_DF_DCSP_COLTIVAZIONI_1,1.0."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Miranda, J.C., Gen\u00e9-Mola, J., Zude-Sasse, M., Tsoulias, N., Escol\u00e0, A., Arn\u00f3, J., Rosell-Polo, J.R., Sanz-Cortiella, R., Mart\u00ednez-Casasnovas, J.A., and Gregorio, E. (2023). Fruit sizing using AI: A review of methods and challenges. Postharvest Biol. Technol., 206.","DOI":"10.1016\/j.postharvbio.2023.112587"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2216","DOI":"10.3390\/agriengineering5040136","article-title":"Development Challenges of Fruit-Harvesting Robotic Arms: A Critical Review","volume":"5","author":"Kaleem","year":"2023","journal-title":"AgriEngineering"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Massaglia, S., Borra, D., Peano, C., Sottile, F., and Merlino, V.M. (2019). Consumer Preference Heterogeneity Evaluation in Fruit and Vegetable Purchasing Decisions Using the Best\u2013Worst Approach. Foods, 8.","DOI":"10.3390\/foods8070266"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Neupane, C., Pereira, M., Koirala, A., and Walsh, K.B. (2023). Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning. Sensors, 23.","DOI":"10.3390\/s23083868"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Walsh, B.B. (2018). Advances in Agricultural Machinery and Technologies, CRC Press. [1st ed.]. Chapter Fruit and Vegetable Packhouse Technologies for Assessing Fruit Quantity and Quality.","DOI":"10.1201\/9781351132398-15"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compag.2015.05.021","article-title":"Sensors and systems for fruit detection and localization: A review","volume":"116","author":"Gongal","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Xiao, F., Wang, H., Xu, Y., and Zhang, R. (2023). Fruit Detection and Recognition Based on Deep Learning for Automatic Harvesting: An Overview and Review. Agronomy, 13.","DOI":"10.3390\/agronomy13061625"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.compag.2018.11.026","article-title":"In-field high throughput grapevine phenotyping with a consumer-grade depth camera","volume":"156","author":"Milella","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105247","DOI":"10.1016\/j.compag.2020.105247","article-title":"Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association","volume":"170","author":"Santos","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mane, S., Bartakke, P., and Bastewad, T. (2023, January 10\u201311). DetSSeg: A Selective On-Field Pomegranate Segmentation Approach. Proceedings of the 2023 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI), Gwalior, India.","DOI":"10.1109\/CVMI59935.2023.10464563"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108700","DOI":"10.1016\/j.engappai.2024.108700","article-title":"PG-YOLO: An efficient detection algorithm for pomegranate before fruit thinning","volume":"134","author":"Wang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, J., Du, C., Li, Y., Mudhsh, M., Guo, D., Fan, Y., Wu, X., Wang, X., and Almodfer, R. (2024). YOLO-Granada: A lightweight attentioned Yolo for pomegranates fruit detection. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-67526-4"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"109791","DOI":"10.1016\/j.scienta.2020.109791","article-title":"A method for organs classification and fruit counting on pomegranate trees based on multi-features fusion and support vector machine by 3D point cloud","volume":"278","author":"Zhang","year":"2021","journal-title":"Sci. Hortic."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1079\/ejhs.2009\/1226350","article-title":"Modelling apple fruit yield using image analysis for fruit colour, shape and texture","volume":"74","author":"Stajnko","year":"2009","journal-title":"Eur. J. Hortic. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0168-1699(03)00086-3","article-title":"Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging","volume":"42","author":"Stajnko","year":"2004","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"126030","DOI":"10.1016\/j.eja.2020.126030","article-title":"Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV","volume":"115","author":"Egea","year":"2020","journal-title":"Eur. J. Agron."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106696","DOI":"10.1016\/j.compag.2022.106696","article-title":"Canopy-attention-YOLOv4-based immature\/mature apple fruit detection on dense-foliage tree architectures for early crop load estimation","volume":"193","author":"Lu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/TAFE.2024.3408912","article-title":"Fruit Monitoring and Harvest Date Prediction Using On-Tree Automatic Image Tracking","volume":"3","author":"Navarro","year":"2025","journal-title":"IEEE Trans. Agrifood Electron."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"M\u00e9ndez, V., P\u00e9rez-Romero, A., Sola-Guirado, R., Miranda-Fuentes, A., Manzano-Agugliaro, F., Zapata-Sierra, A., and Rodr\u00edguez-Lizana, A. (2019). In-Field Estimation of Orange Number and Size by 3D Laser Scanning. Agronomy, 9.","DOI":"10.3390\/agronomy9120885"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.biosystemseng.2023.07.010","article-title":"Simultaneous fruit detection and size estimation using multitask deep neural networks","volume":"233","author":"Gregorio","year":"2023","journal-title":"Biosyst. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106343","DOI":"10.1016\/j.compag.2021.106343","article-title":"In-field apple size estimation using photogrammetry-derived 3D point clouds: Comparison of 4 different methods considering fruit occlusions","volume":"188","author":"Gregorio","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, Z., Walsh, K.B., and Verma, B. (2017). On-Tree Mango Fruit Size Estimation Using RGB-D Images. Sensors, 17.","DOI":"10.3390\/s17122738"},{"key":"ref_26","unstructured":"(2025, September 05). Aerobotics. Available online: https:\/\/aerobotics.com\/."},{"key":"ref_27","unstructured":"(2025, September 05). Green Atlas. Available online: https:\/\/greenatlas.com\/."},{"key":"ref_28","unstructured":"(2025, September 05). Tevel. Available online: https:\/\/www.tevel-tech.com\/."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s43154-020-00034-1","article-title":"Selective Harvesting Robotics: Current Research, Trends, and Future Directions","volume":"2","author":"Kootstra","year":"2021","journal-title":"Curr. Robot. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Benos, L., Moysiadis, V., Kateris, D., Tagarakis, A.C., Busato, P., Pearson, S., and Bochtis, D. (2023). Human\u2013Robot Interaction in Agriculture: A Systematic Review. Sensors, 23.","DOI":"10.3390\/s23156776"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.biosystemseng.2020.09.009","article-title":"Safety and ergonomics in human-robot interactive agricultural operations","volume":"200","author":"Benos","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Devanna, R.P., Milella, A., Marani, R., Garofalo, S.P., Vivaldi, G.A., Pascuzzi, S., Galati, R., and Reina, G. (2022). In-Field Automatic Identification of Pomegranates Using a Farmer Robot. Sensors, 22.","DOI":"10.3390\/s22155821"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107233","DOI":"10.1016\/j.compag.2022.107233","article-title":"Mature pomegranate fruit detection and location combining improved F-PointNet with 3D point cloud clustering in orchard","volume":"200","author":"Yu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.scienta.2007.05.008","article-title":"Mass modeling of pomegranate (Punica granatum L.) fruit with some physical characteristics","volume":"114","author":"Khoshnam","year":"2007","journal-title":"Sci. Hortic."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"075001","DOI":"10.1115\/1.4067624","article-title":"On the Climbing Ability of Passively Suspended Tracked Robots","volume":"17","author":"Reina","year":"2025","journal-title":"J. Mech. Robot."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Keselman, L., Woodfill, J.I., Grunnet-Jepsen, A., and Bhowmik, A. (2017, January 21\u201326). Intel(R) RealSense(TM) Stereoscopic Depth Cameras. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.167"},{"key":"ref_37","unstructured":"Haralick, R.M., and Shapiro, L.G. (1992). Computer and Robot Vision, Addison\u2013Wesley."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"47942","DOI":"10.1109\/ACCESS.2024.3381511","article-title":"Robot-as-a-Service as a New Paradigm in Precision Farming","volume":"12","author":"Milella","year":"2024","journal-title":"IEEE Access"}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/14\/10\/131\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:47:47Z","timestamp":1760035667000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/14\/10\/131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["robotics14100131"],"URL":"https:\/\/doi.org\/10.3390\/robotics14100131","relation":{},"ISSN":["2218-6581"],"issn-type":[{"type":"electronic","value":"2218-6581"}],"subject":[],"published":{"date-parts":[[2025,9,23]]}}}