{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T20:43:16Z","timestamp":1778618596477,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014141","name":"EAFRD","doi-asserted-by":"publisher","award":["PDR2020-101-031358"],"award-info":[{"award-number":["PDR2020-101-031358"]}],"id":[{"id":"10.13039\/501100014141","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/00151\/2020"],"award-info":[{"award-number":["UIDB\/00151\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Climate"],"abstract":"<jats:p>Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.<\/jats:p>","DOI":"10.3390\/cli10020011","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T22:46:32Z","timestamp":1642545992000},"page":"11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Peaches Detection Using a Deep Learning Technique\u2014A Contribution to Yield Estimation, Resources Management, and Circular Economy"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6027-7763","authenticated-orcid":false,"given":"Eduardo T.","family":"Assun\u00e7\u00e3o","sequence":"first","affiliation":[{"name":"C-MAST Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 6201-001 Covilha, Portugal"},{"name":"Deparment of Electromechanical Engineering, University of Beira Interior, Rua Marqu\u00eas d\u2019\u00c1vila e Bolama, 6201-001 Covilha, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1691-1709","authenticated-orcid":false,"given":"Pedro D.","family":"Gaspar","sequence":"additional","affiliation":[{"name":"C-MAST Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 6201-001 Covilha, Portugal"},{"name":"Deparment of Electromechanical Engineering, University of Beira Interior, Rua Marqu\u00eas d\u2019\u00c1vila e Bolama, 6201-001 Covilha, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8599-6737","authenticated-orcid":false,"given":"Ricardo J. M.","family":"Mesquita","sequence":"additional","affiliation":[{"name":"C-MAST Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 6201-001 Covilha, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6599-0688","authenticated-orcid":false,"given":"Maria P.","family":"Sim\u00f5es","sequence":"additional","affiliation":[{"name":"School of Agriculture, Polytechnic Institute of Castelo Branco, 6000-084 Castelo Branco, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3041-0196","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Ramos","sequence":"additional","affiliation":[{"name":"School of Agriculture, Polytechnic Institute of Castelo Branco, 6000-084 Castelo Branco, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2551-8570","authenticated-orcid":false,"given":"Hugo","family":"Proen\u00e7a","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Department of Computer Science, University of Beira Interior, 6201-001 Covilha, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8221-0666","authenticated-orcid":false,"given":"Pedro R. M.","family":"Inacio","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Department of Computer Science, University of Beira Interior, 6201-001 Covilha, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"key":"ref_1","unstructured":"Ontario Ministry of Agriculture (2021, October 11). Introduction to Sustainable Agriculture, Available online: http:\/\/www.omafra.gov.on.ca\/english\/busdev\/facts\/15-023.htm."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Balafoutis, A., Beck, B., Fountas, S., Vangeyte, J., van der Wal, T., Soto, I., G\u00f3mez-Barbero, M., Barnes, A.P., and Eory, V. (2017). Precision Agriculture Technologies positively contributing to GHG emissions mitigation, farm productivity and economics. Sustainability, 9.","DOI":"10.3390\/su9081339"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alibabaei, K., Gaspar, P., and Lima, T.M. (2020, January 8\u20139). Modeling evapotranspiration using Encoder-Decoder Model. Proceedings of the 2020 International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain.","DOI":"10.1109\/DASA51403.2020.9317100"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Assun\u00e7\u00e3o, E., Diniz, C., Gaspar, P., and Proen\u00e7a, H. (2020, January 8\u20139). Decision-making support system for fruit diseases classification using Deep Learning. Proceedings of the 2020 International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain.","DOI":"10.1109\/DASA51403.2020.9317219"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"H\u00e4ni, N., Roy, P., and Isler, V. (2018, January 1\u20135). Apple Counting using Convolutional Neural Networks. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594304"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.compag.2017.05.019","article-title":"An yield estimation in citrus orchards via fruit detection and counting using image processing","volume":"140","author":"Dorj","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","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_8","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_9","doi-asserted-by":"crossref","unstructured":"Desai, J.P., 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_10","doi-asserted-by":"crossref","unstructured":"Puttemans, S., Vanbrabant, Y., Tits, L., and Goedem\u00e9, T. (2016, January 12\u201315). Automated visual fruit detection for harvest estimation and robotic harvesting. Proceedings of the 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), Oulu, Finland.","DOI":"10.1109\/IPTA.2016.7820996"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1002\/rob.21699","article-title":"Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards","volume":"34","author":"Bargoti","year":"2017","journal-title":"J. Field Robot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"H\u00e4ni, N., Roy, P., and Isler, V. (2020). A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards. arXiv.","DOI":"10.1002\/rob.21902"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1007\/s11119-019-09642-0","article-title":"Deep learning for real-time fruit detection and orchard fruit load estimation: Benchmarking of \u2018MangoYOLO\u2019","volume":"20","author":"Koirala","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"139635","DOI":"10.1109\/ACCESS.2019.2942144","article-title":"Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201326). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C.Y., and Berg, A. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the ECCV 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R.B., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, S., and Deng, W. (2015, January 3\u20136). Very deep convolutional neural network based image classification using small training sample size. Proceedings of the 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ACPR.2015.7486599"},{"key":"ref_24","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_26","unstructured":"Yu, H., Chen, C., Du, X., Li, Y., Rashwan, A., Hou, L., Jin, P., Yang, F., Liu, F., and Kim, J. (2021, October 12). TensorFlow Model Garden. Available online: https:\/\/github.com\/tensorflow\/models."},{"key":"ref_27","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the ECCV 2014, Zurich, Switzerland."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"}],"container-title":["Climate"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2225-1154\/10\/2\/11\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:03:27Z","timestamp":1760133807000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2225-1154\/10\/2\/11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,18]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["cli10020011"],"URL":"https:\/\/doi.org\/10.3390\/cli10020011","relation":{},"ISSN":["2225-1154"],"issn-type":[{"value":"2225-1154","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,18]]}}}