{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T23:50:37Z","timestamp":1774655437315,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T00:00:00Z","timestamp":1723680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Higher Education, Scientific Research and Innovation of Morocco (MESRSI)","award":["Alkhawarizmi\/2020\/37"],"award-info":[{"award-number":["Alkhawarizmi\/2020\/37"]}]},{"name":"Ministry of Higher Education, Scientific Research and Innovation of Morocco (MESRSI)","award":["UIDB\/04516\/2020"],"award-info":[{"award-number":["UIDB\/04516\/2020"]}]},{"name":"National Centre of Scientific and Technical Research of Morocco (CNRST)","award":["Alkhawarizmi\/2020\/37"],"award-info":[{"award-number":["Alkhawarizmi\/2020\/37"]}]},{"name":"National Centre of Scientific and Technical Research of Morocco (CNRST)","award":["UIDB\/04516\/2020"],"award-info":[{"award-number":["UIDB\/04516\/2020"]}]},{"name":"Digital Development Agency of Morocco (ADD) through the AL-KHAWARIZMI program of Morocco","award":["Alkhawarizmi\/2020\/37"],"award-info":[{"award-number":["Alkhawarizmi\/2020\/37"]}]},{"name":"Digital Development Agency of Morocco (ADD) through the AL-KHAWARIZMI program of Morocco","award":["UIDB\/04516\/2020"],"award-info":[{"award-number":["UIDB\/04516\/2020"]}]},{"name":"NOVA LINCS","award":["Alkhawarizmi\/2020\/37"],"award-info":[{"award-number":["Alkhawarizmi\/2020\/37"]}]},{"name":"NOVA LINCS","award":["UIDB\/04516\/2020"],"award-info":[{"award-number":["UIDB\/04516\/2020"]}]},{"name":"FCT.IP","award":["Alkhawarizmi\/2020\/37"],"award-info":[{"award-number":["Alkhawarizmi\/2020\/37"]}]},{"name":"FCT.IP","award":["UIDB\/04516\/2020"],"award-info":[{"award-number":["UIDB\/04516\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The real-time detection of fruits and plants is a crucial aspect of digital agriculture, enhancing farming efficiency and productivity. This study addresses the challenge of embedding a real-time strawberry detection system in a small mobile robot operating within a greenhouse environment. The embedded system is based on the YOLO architecture running in a single GPU card, with the Open Neural Network Exchange (ONNX) representation being employed to accelerate the detection process. The experiments conducted in this study demonstrate that the proposed model achieves a mean average precision (mAP) of over 97%, processing eight frames per second for 512 \u00d7 512 pixel images. These results affirm the utility of the proposed approach in detecting strawberry plants in order to optimize the spraying process and avoid inflicting any harm on the plants. The goal of this research is to highlight the potential of integrating advanced detection algorithms into small-scale robotics, providing a viable solution for enhancing precision agriculture practices.<\/jats:p>","DOI":"10.3390\/app14167195","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T06:23:20Z","timestamp":1723789400000},"page":"7195","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Embedding a Real-Time Strawberry Detection Model into a Pesticide-Spraying Mobile Robot for Greenhouse Operation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3854-5721","authenticated-orcid":false,"given":"Khalid","family":"El Amraoui","sequence":"first","affiliation":[{"name":"LCS Laboratory, Physics Department, Faculty of Sciences, Mohammed 5 University in Rabat, Ibn Battouta Street, Rabat 10000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5394-9066","authenticated-orcid":false,"given":"Mohamed","family":"El Ansari","sequence":"additional","affiliation":[{"name":"Informatics and Applications Laboratory, Faculty of Science, Moulay Ismail University in Meknes, Zitoune Street, Meknes 11201, Morocco"}]},{"given":"Mouataz","family":"Lghoul","sequence":"additional","affiliation":[{"name":"LCS Laboratory, Physics Department, Faculty of Sciences, Mohammed 5 University in Rabat, Ibn Battouta Street, Rabat 10000, Morocco"}]},{"given":"Mustapha","family":"El Alaoui","sequence":"additional","affiliation":[{"name":"LCS Laboratory, Physics Department, Faculty of Sciences, Mohammed 5 University in Rabat, Ibn Battouta Street, Rabat 10000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1200-9208","authenticated-orcid":false,"given":"Abdelkrim","family":"Abanay","sequence":"additional","affiliation":[{"name":"IRSM, High Institute of Management, Administration, and Computer Engineering (ISMAGI), Rabat 10120, Morocco"}]},{"given":"Bouazza","family":"Jabri","sequence":"additional","affiliation":[{"name":"LCS Laboratory, Physics Department, Faculty of Sciences, Mohammed 5 University in Rabat, Ibn Battouta Street, Rabat 10000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8857-4249","authenticated-orcid":false,"given":"Lhoussaine","family":"Masmoudi","sequence":"additional","affiliation":[{"name":"LCS Laboratory, Physics Department, Faculty of Sciences, Mohammed 5 University in Rabat, Ibn Battouta Street, Rabat 10000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5337-5699","authenticated-orcid":false,"given":"Jos\u00e9","family":"Valente de Oliveira","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Campus de Gambelas, Universidade do Algarve, 8005-391 Faro, Portugal"},{"name":"NOVA-LINCS, and Center of Intelligent Systems, IDMEC\/LAETA, University of Lisbon, 1049-001 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"ref_1","unstructured":"Food and Agriculture Organization of the United Nations (2017). The Future of Food and Agriculture: Trends and Challenges, Food and Agriculture Organization of the United Nations."},{"key":"ref_2","unstructured":"(2024, February 06). Morocco\u2014GDP Distribution Across Economic Sectors 2012\u20132022. Statista. Available online: https:\/\/www.statista.com\/statistics\/502771\/morocco-gdp-distribution-across-economic-sectors\/."},{"key":"ref_3","unstructured":"(2024, February 06). Morocco: Food Share in Merchandise Exports. Statista. Available online: https:\/\/www.statista.com\/statistics\/1218971\/food-share-in-merchandise-exports-in-morocco\/."},{"key":"ref_4","unstructured":"Oluwole, V. (2024, February 06). Morocco\u2019s Fresh Strawberry Exports Generate up to $70 Million in Annual Revenue. Available online: https:\/\/africa.businessinsider.com\/local\/markets\/moroccos-fresh-strawberry-exports-generate-up-to-dollar70-million-in-annual-revenue\/yrkgkzv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105174","DOI":"10.1016\/j.compag.2019.105174","article-title":"Crop pest recognition in natural scenes using convolutional neural networks","volume":"169","author":"Li","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0065-2113(08)60513-1","article-title":"Aspects of Precision Agriculture","volume":"Volume 67","author":"Sparks","year":"1999","journal-title":"Advances in Agronomy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compag.2016.04.024","article-title":"A survey of image processing techniques for plant extraction and segmentation in the field","volume":"125","author":"Hamuda","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","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_9","first-page":"41","article-title":"Detection of plant leaf diseases using image segmentation and soft computing techniques","volume":"4","author":"Singh","year":"2017","journal-title":"Inf. Process. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.ifacol.2018.08.183","article-title":"Mature Tomato Fruit Detection Algorithm Based on improved HSV and Watershed Algorithm","volume":"51","author":"Malik","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, X., Vladislav, Z., Viktor, O., Wu, Z., and Zhao, M. (2022). Online recognition and yield estimation of tomato in plant factory based on YOLOv3. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-12732-1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Treboux, J., and Genoud, D. (2018, January 4\u20137). Improved Machine Learning Methodology for High Precision Agriculture. Proceedings of the 2018 Global Internet of Things Summit (GIoTS), Bilbao, Spain.","DOI":"10.1109\/GIOTS.2018.8534558"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Lamb, N., and Chuah, M.C. (2018, January 10\u201313). A Strawberry Detection System Using Convolutional Neural Networks. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622466"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104846","DOI":"10.1016\/j.compag.2019.06.001","article-title":"Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN","volume":"163","author":"Yu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_19","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1007\/s11119-020-09754-y","article-title":"Fast and accurate detection of kiwifruit in orchard using improved YOLOv3-tiny model","volume":"22","author":"Fu","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"42","DOI":"10.37221\/eaef.13.2_42","article-title":"An Aerial Weed Detection System for Green Onion Crops Using the You Only Look Once (YOLOv3) Deep Learning Algorithm","volume":"13","author":"Parico","year":"2020","journal-title":"Eng. Agric. Environ. Food"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, G., Nouaze, J.C., Touko Mbouembe, P.L., and Kim, J.H. (2020). YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3. Sensors, 20.","DOI":"10.3390\/s20072145"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.ifacol.2022.11.110","article-title":"Detecting and Localizing Strawberry Centers for Robotic Harvesting in Field Environment","volume":"55","author":"He","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xie, Z., Chen, R., Lin, C., and Zeng, J. (2024, February 06). A Lightweight Real-Time Method for Strawberry Ripeness Detection Based on Improved Yolo. Available online: https:\/\/ssrn.com\/abstract=4570965.","DOI":"10.2139\/ssrn.4570965"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106586","DOI":"10.1016\/j.compag.2021.106586","article-title":"Real-time strawberry detection using deep neural networks on embedded system (rtsd-net): An edge AI application","volume":"192","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.optlastec.2018.08.007","article-title":"Fast vehicle logo detection in complex scenes","volume":"110","author":"Yang","year":"2019","journal-title":"Opt. Laser Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.matpr.2021.03.174","article-title":"Design and development of a robot for spraying fertilizers and pesticides for agriculture","volume":"81","author":"Ghafar","year":"2023","journal-title":"Mater. Today Proc."},{"key":"ref_28","first-page":"70","article-title":"Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments","volume":"11","author":"Ma","year":"2024","journal-title":"Artif. Intell. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108728","DOI":"10.1016\/j.compag.2024.108728","article-title":"Optimizing tomato plant phenotyping detection: Boosting YOLOv8 architecture to tackle data complexity","volume":"218","author":"Solimani","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s13640-021-00559-1","article-title":"OSDDY: Embedded system-based object surveillance detection system with small drone using deep YOLO","volume":"2021","author":"Madasamy","year":"2021","journal-title":"J. Image Video Proc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"317","DOI":"10.3934\/electreng.2022019","article-title":"LIDAR-based autonomous navigation method for an agricultural mobile robot in strawberry greenhouse: AgriEco Robot","volume":"6","author":"Abanay","year":"2022","journal-title":"AIMS Electron. Electr. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"168254","DOI":"10.1016\/j.ijleo.2021.168254","article-title":"A calibration method of 2D LIDAR-Visual sensors embedded on an agricultural robot","volume":"249","author":"Abanay","year":"2022","journal-title":"Optik"},{"key":"ref_33","first-page":"61","article-title":"Comparative Study of ROS on Embedded System for a Mobile Robot","volume":"12","author":"Min","year":"2018","journal-title":"J. Autom. Mob. Robot. Intell. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Amert, T., Otterness, N., Yang, M., Anderson, J.H., and Smith, F.D. (2017, January 5\u20138). GPU Scheduling on the NVIDIA TX2: Hidden Details Revealed. Proceedings of the 2017 IEEE Real-Time Systems Symposium (RTSS), Paris, France.","DOI":"10.1109\/RTSS.2017.00017"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1186\/s13007-024-01226-y","article-title":"Field cabbage detection and positioning system based on improved YOLOv8n","volume":"20","author":"Jiang","year":"2024","journal-title":"Plant Methods"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhou, J., Hu, W., Zou, A., Zhai, S., Liu, T., Yang, W., and Jiang, P. (2022). Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S. Agriculture, 12.","DOI":"10.3390\/agriculture12070993"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the Computer Vision\u2014ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2"},{"key":"ref_38","first-page":"1","article-title":"YOLOv3: An Incremental Improvement","volume":"1804","author":"Redmon","year":"2018","journal-title":"Comput. Vis. Pattern Recognit."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Beyaz, A., and G\u00fcl, V. (2023). YOLOv4 and Tiny YOLOv4 Based Forage Crop Detection with an Artificial Intelligence Board. Braz. Arch. Biol. Technol., 66.","DOI":"10.1590\/1678-4324-2023220803"},{"key":"ref_40","unstructured":"Li, C., Zhang, B., Li, L., Li, L., Geng, Y., Cheng, M., Xiaoming, X., Chu, X., and Wei, X. (2024, January 7\u201311). Yolov6: A single-stage object detection framework for industrial applications. Proceedings of the Twelfth International Conference on Learning Representations (ICLR2024), Vienna, Austria. Available online: https:\/\/openreview.net\/forum?id=7c3ZOKGQ6s."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ponnusamy, V., Coumaran, A., Shunmugam, A.S., Rajaram, K., and Senthilvelavan, S. (2020, January 28\u201330). Smart Glass: Real-Time Leaf Disease Detection using YOLO Transfer Learning. Proceedings of the 2020 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India.","DOI":"10.1109\/ICCSP48568.2020.9182146"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Qin, Z., Wang, W., Dammer, K.-H., Guo, L., and Cao, Z. (2021). Ag-YOLO: A Real-Time Low-Cost Detector for Precise Spraying with Case Study of Palms. Front. Plant Sci., 12.","DOI":"10.3389\/fpls.2021.753603"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Buzzy, M., Thesma, V., Davoodi, M., and Mohammadpour Velni, J. (2020). Real-Time Plant Leaf Counting Using Deep Object Detection Networks. Sensors, 20.","DOI":"10.3390\/s20236896"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"46723","DOI":"10.1109\/ACCESS.2020.2978912","article-title":"Efficient Foreign Object Detection Between PSDs and Metro Doors via Deep Neural Networks","volume":"8","author":"Dai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_45","unstructured":"Wu, D., Wang, Y., Xia, S.-T., Bailey, J., and Ma, X. (2020, January 30). Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets. Proceedings of the 2020 International Conference on Learning Representations, Addis Ababa, Ethiopia. Available online: https:\/\/openreview.net\/forum?id=BJlRs34Fvr."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"17723","DOI":"10.1007\/s00521-022-07419-7","article-title":"R2U++: A multiscale recurrent residual U-Net with dense skip connections for medical image segmentation","volume":"34","author":"Mubashar","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.neucom.2018.12.075","article-title":"Dilated Residual Networks with Symmetric Skip Connection for image denoising","volume":"345","author":"Peng","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, H., Hu, Z., Guo, Y., Yang, Z., Zhou, F., and Xu, P. (2020). A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3. Appl. Sci., 10.","DOI":"10.3390\/app10196732"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 16\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"},{"key":"ref_50","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_51","doi-asserted-by":"crossref","unstructured":"Sun, C., Shrivastava, A., Singh, S., and Gupta, A. (2017, January 22\u201329). Revisiting Unreasonable Effectiveness of Data in Deep Learning Era. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.97"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.compag.2019.01.012","article-title":"Apple detection during different growth stages in orchards using the improved YOLO-V3 model","volume":"157","author":"Tian","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_54","unstructured":"Skalski, P. (2023, July 18). Makesense.Ai. Available online: https:\/\/github.com\/SkalskiP\/make-sense."},{"key":"ref_55","unstructured":"Singh, M., Kang, D.-K., Lee, J.-H., Tiwary, U.S., Singh, D., and Chung, W.-Y. (2020, January 24\u201326). A Novel Diminish Smooth L1 Loss Model with Generative Adversarial Network. Proceedings of the Intelligent Human Computer Interaction, Daegu, Republic of Korea."}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/14\/16\/7195\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:37:21Z","timestamp":1760110641000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/14\/16\/7195"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,15]]},"references-count":55,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["app14167195"],"URL":"https:\/\/doi.org\/10.3390\/app14167195","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,15]]}}}