{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:52:10Z","timestamp":1781589130702,"version":"3.54.5"},"reference-count":37,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>\n                    Accurate identification of plant-parasitic nematodes is fundamental to effective crop protection and the maintenance of soil ecosystem integrity. This study integrates morphological characterization with deep learning-based object detection to enhance diagnostic accuracy for economically important nematode genera,\n                    <jats:italic>Xiphinema<\/jats:italic>\n                    and\n                    <jats:italic>Mesocriconema<\/jats:italic>\n                    , associated with vineyards. Three advanced YOLO architectures [YOLO-NAS, YOLOv11, and Roboflow 3.0 (YOLOv8 architecture)] were trained and evaluated on a high-resolution annotated microscopic image dataset consisting of 961 images and 1.034 bounding-box annotations. Although the target nematode genera display considerable morphological variability and genetic divergence among populations, the present investigation focused on genus-level detection of\n                    <jats:italic>M. xenoplax<\/jats:italic>\n                    and\n                    <jats:italic>X. pachtaicum<\/jats:italic>\n                    . These two major ectoparasitic nematodes cause significant damage to grapevine root systems. Among the models tested, YOLOv11 achieved the highest detection accuracy, with a precision of 95.7 % and an mAP@50 of 93.2 %. YOLO-NAS exhibited comparable performance (mAP@50 = 92.7 %, precision = 93.1 %, recall = 84.9 %), while Roboflow 3.0 (YOLOv8 architecture) yielded satisfactory results (mAP@50 = 89.4 %), indicating its applicability for real-time diagnostic workflows. This integration of taxonomic expertise with deep learning represents a new methodological framework for nematode identification. All models exhibited rapid convergence and stable learning dynamics during training. The findings underscore the potential of YOLO-based frameworks as efficient, scalable, and reproducible tools that complement classical morphological and molecular identification, contributing to precision agriculture and sustainable nematode management strategies.\n                  <\/jats:p>","DOI":"10.2478\/helm-2026-0002","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T01:22:15Z","timestamp":1777512135000},"page":"38-47","source":"Crossref","is-referenced-by-count":0,"title":["Integrating morphological and deep learning approaches for the identification of economically important nematode genera in vineyards:\n                    <i>Mesocriconema<\/i>\n                    and\n                    <i>Xiphinema<\/i>"],"prefix":"10.2478","volume":"63","author":[{"given":"L.","family":"\u00d6ZT\u00dcRK","sequence":"first","affiliation":[{"name":"1 Independent researcher , , S\u00fcleymanpa\u015fa, Tekirda\u011f , T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"\u015e\u0130N","sequence":"additional","affiliation":[{"name":"2 Plant Protection Department, Faculty of Agriculture, Sakarya University of Applied Science , , Sakarya , T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"key":"2026061604590361186_j_helm-2026-0002_ref_001","doi-asserted-by":"crossref","unstructured":"Abid, M.S.Z., Jahan, B., Al Mamun, A., Hossen, M.J., Mazumder, S.H. (2024): Bangladeshi crops leaf disease detection using YOLOv8. Heliyon, 10: e36694","DOI":"10.1016\/j.heliyon.2024.e36694"},{"key":"2026061604590361186_j_helm-2026-0002_ref_002","doi-asserted-by":"crossref","unstructured":"Ahmad, I., Yang, Y., Yue, Y., Ye, C., Hassan, M., Cheng, X., Wu, Y., Zhang, Y. (2022): Deep learning based detector YOLOv5 for identifying insect pests. Appl Sci, 12(19): 10167. DOI: 10.3390\/app121910167","DOI":"10.3390\/app121910167"},{"key":"2026061604590361186_j_helm-2026-0002_ref_003","doi-asserted-by":"crossref","unstructured":"Aldakheel, E.A., Zakariah, M., Alabdalall, A.H. (2024): Detection and identification of plant leaf diseases using YOLOv4. Front Plant Sci, 15: 1355941. DOI: 10.3389\/fpls.2024.1355941","DOI":"10.3389\/fpls.2024.1355941"},{"key":"2026061604590361186_j_helm-2026-0002_ref_004","doi-asserted-by":"crossref","unstructured":"Aleman, J.S., Reyes-Duke, A.M. (2025): Performance Analysis of Roboflow 3.0 and YOLO-NAS in PCB Defect Detection Compared to YOLOv9 and RT-DETR Using Augmented Image Dataset. In: 2025 10th International Conference on Control and Robotics Engineering (ICCRE), IEEE, pp. 229\u2013234. DOI: 10.1109\/IC-CRE65455.2025.11093496","DOI":"10.1109\/ICCRE65455.2025.11093496"},{"key":"2026061604590361186_j_helm-2026-0002_ref_005","doi-asserted-by":"crossref","unstructured":"Bjerge, K., Alison, J., Dyrmann, M., Frigaard, C.E., Mann, H.M.R., H\u00f8ye, T.T. (2023): Accurate detection and identification of insects from camera trap images with deep learning. PLOS Sustain Transform, 2(3): e0000051. DOI: 10.1371\/journal.pstr.0000051","DOI":"10.1371\/journal.pstr.0000051"},{"key":"2026061604590361186_j_helm-2026-0002_ref_006","unstructured":"Brown, D.J.F., Boag, B. (1988): An examination of methods used to extract virus-vector nematodes (Nematoda: Longidoridae and Trichodoridae) from soil samples. Nematol Mediterr, 16: 93\u201399"},{"key":"2026061604590361186_j_helm-2026-0002_ref_007","doi-asserted-by":"crossref","unstructured":"Dai, M., Dorjoy, M.M.H., Miao, H., Zhang, S. (2023): A new pest detection method based on improved YOLOv5m. Insects, 14(1): 54. DOI: 10.3390\/insects14010054","DOI":"10.3390\/insects14010054"},{"key":"2026061604590361186_j_helm-2026-0002_ref_008","unstructured":"De Grisse, A.T. (1969): Contribution to the morphology and systematics of the Criconematidae (Taylor, 1936) Thorne, 1949. PhD thesis, Belgium, Ghent: Ghent University Press, 131 pp."},{"key":"2026061604590361186_j_helm-2026-0002_ref_009","doi-asserted-by":"crossref","unstructured":"Deimi, A.M., Mitkowski, N.A. (2010): Nematodes associated with vineyards throughout Markazi Province (Arak), Iran. Australas Plant Pathol, 39(6): 571\u2013577. DOI: 10.1071\/AP10044","DOI":"10.1071\/AP10044"},{"key":"2026061604590361186_j_helm-2026-0002_ref_010","unstructured":"European Mediterranean Plant Protection Organization (2025): EPPO A1 list of pests recommended for regulation as quarantine pests (version 2025-09). Available at: https:\/\/www.eppo.int\/ACTIV-ITIES\/plant_quarantine\/A1_list"},{"key":"2026061604590361186_j_helm-2026-0002_ref_011","unstructured":"Ferris, H., Mckenry, M.V., Jaffee, B.A., Anderson, C.E., Juurma, A. (2004): Population characteristics and dosage trajectory analysis for Mesocriconema xenoplax in California Prunus orchards. J Nematol, 36(4): 505\u2013516"},{"key":"2026061604590361186_j_helm-2026-0002_ref_012","doi-asserted-by":"crossref","unstructured":"Gao, L., Cao, H., Zou, H., Wu, H. (2025): DMN-YOLO: A robust YOLOv11 model for detecting apple leaf diseases in complex field conditions. Agriculture, 15(11): 1138","DOI":"10.3390\/agriculture15111138"},{"key":"2026061604590361186_j_helm-2026-0002_ref_013","doi-asserted-by":"crossref","unstructured":"Guo, C., Zheng, S., Cheng, G., Zhang, Y., Ding, J. (2023): An improved YOLOv4 used for grape detection in unstructured environment. Front Plant Sci, 14: 1209910. DOI: 10.3389\/fpls.2023.1209910","DOI":"10.3389\/fpls.2023.1209910"},{"key":"2026061604590361186_j_helm-2026-0002_ref_014","doi-asserted-by":"crossref","unstructured":"Gupta, C., Gill, N.S., Gulia, P., Kumar, A., Karamti, H., Moges, D.M. (2025): An optimized YOLO NAS based framework for realtime object detection. Sci Rep, 15(1): 32903. DOI: 10.1038\/s41598-025-17919-w","DOI":"10.1038\/s41598-025-17919-w"},{"key":"2026061604590361186_j_helm-2026-0002_ref_015","unstructured":"Jegham, N., Koh, C.Y., Abdelatti, M. (2024): Evaluating the evolution of YOLO (You Only Look Once) models: A comprehensive benchmark study of YOLO11 and its predecessors. arXiv, arX-iv:2411.00201"},{"key":"2026061604590361186_j_helm-2026-0002_ref_016","doi-asserted-by":"crossref","unstructured":"Kaur, R., Singh, S. (2023): A comprehensive review of object detection with deep learning. Digit Signal Process, 132: 103812. DOI: 10.1016\/j.dsp.2022.103812","DOI":"10.1016\/j.dsp.2022.103812"},{"key":"2026061604590361186_j_helm-2026-0002_ref_017","unstructured":"Khanam, R., Hussain, M. (2024): Yolov11: An overview of the key architectural enhancements. arXiv, arXiv:2410.17725"},{"key":"2026061604590361186_j_helm-2026-0002_ref_018","doi-asserted-by":"crossref","unstructured":"Kumari, S., Li\u0161kov\u00e1, M. (2009): Molecular confirmation of Xiphinema italiae Meyl 1953( Nematoda: Longidoridae) from the Slovak Republic. Helminthologia, 46(2): 131\u2013134. DOI: 10.2478\/s11687-009-0025-8","DOI":"10.2478\/s11687-009-0025-8"},{"key":"2026061604590361186_j_helm-2026-0002_ref_019","doi-asserted-by":"crossref","unstructured":"Kumari, S., Decraemer, W., De Luca, F. (2010): Molecular characterization of Xiphinema brevicollum (Nematoda: Longidoridae) from the Czech Republic. Eur J Plant Pathol, 128(2): 243\u2013250","DOI":"10.1007\/s10658-010-9651-8"},{"key":"2026061604590361186_j_helm-2026-0002_ref_020","unstructured":"Lamberti, F., Molinari, S., Moens, M., Brown, D.J.F. (2000): The Xiphinema americanum group. I. Putative species, their geographical occurrence and distribution, and regional polytomous identification keys for the group. Russ J Nematol, 8: 65\u201384"},{"key":"2026061604590361186_j_helm-2026-0002_ref_021","doi-asserted-by":"crossref","unstructured":"Loof, P.A., Luc, M. (1990): A revised polytomous key for the identification of species of the genus Xiphinema Cobb, 1913 (Nematoda: Longidoridae) with exclusion of the X. americanum group. Syst Parasitol, 16(1): 35\u201366","DOI":"10.1007\/BF00009600"},{"key":"2026061604590361186_j_helm-2026-0002_ref_022","doi-asserted-by":"crossref","unstructured":"Mejias, J., Kozlowski, D.K.L., Goshon, J., Maier, R.T., Baum, J.T. (2025): A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines. Sci Rep, 15: 4468. DOI: 10.1038\/s41598-025-88289-6","DOI":"10.1038\/s41598-025-88289-6"},{"key":"2026061604590361186_j_helm-2026-0002_ref_023","doi-asserted-by":"crossref","unstructured":"Omer, S.M., Ghafoor, K.Z., Askar, S.K. (2024): Lightweight improved YOLOv5 model for cucumber leaf disease and pest detection based on deep learning. Signal Image Video Process, 18(2): 1329\u20131342. DOI: 10.1007\/s11760-023-02865-9","DOI":"10.1007\/s11760-023-02865-9"},{"key":"2026061604590361186_j_helm-2026-0002_ref_024","unstructured":"Pinkerton, J.N., Schreiner, R.P., Ivors, K.L., Vasconcelos, M.C. (2004): Effects of Mesocriconema xenoplax on Vitis vinifera and associated mycorrhizal fungi. J Nematol, 36(2): 193\u2013201"},{"key":"2026061604590361186_j_helm-2026-0002_ref_025","doi-asserted-by":"crossref","unstructured":"Pun, T.B., Neupane, A., Koech, R., Owen, K.J. (2022): Detection and quantification of root-knot nematode (Meloidogyne spp.) eggs from tomato plants using image analysis. IEEE Access, 10: 23190\u2013123204","DOI":"10.1109\/ACCESS.2022.3223707"},{"key":"2026061604590361186_j_helm-2026-0002_ref_026","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A. (2016): You only look once: Unified, real-time object detection. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp. 779\u2013788. DOI: 10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"2026061604590361186_j_helm-2026-0002_ref_027","doi-asserted-by":"crossref","unstructured":"Rodriguez-Lira, D.C., C\u00f3rdova-Esparza, D.M., \u00c1lvarez-Alvarado, J.M., Romero-Gonz\u00e1lez, J.A., Terven, J., Rodriguez-Res\u00e9ndiz, J. (2024): Comparative analysis of YOLO models for bean leaf disease detection in natural environments. AgriEngineering, 6(4): 4585\u20134603. DOI: 10.3390\/agriengineering6040262","DOI":"10.3390\/agriengineering6040262"},{"key":"2026061604590361186_j_helm-2026-0002_ref_028","doi-asserted-by":"crossref","unstructured":"Sapkota, R., Flores-Calero, M., Qureshi, R., Badgujar, C., Nepal, U., Poulose, A., Zeno, P., Vaddevolu, U.B.P., Khan, S., Shoman, M., Yan, H. (2025): YOLO advances to its genesis: A decadal and comprehensive review of the You Only Look Once (YOLO) series. Artif Intell Rev, 58(9): 274. DOI: 10.1007\/s10462-025-11253-3","DOI":"10.1007\/s10462-025-11253-3"},{"key":"2026061604590361186_j_helm-2026-0002_ref_029","doi-asserted-by":"crossref","unstructured":"Seinhorst, J.W. (1959): A rapid method for the transfer of nematodes from fixative to anhydrous glycerin. Nematologica, 4: 67\u201369","DOI":"10.1163\/187529259X00381"},{"key":"2026061604590361186_j_helm-2026-0002_ref_030","doi-asserted-by":"crossref","unstructured":"Soeb, M.J.A., Jubayer, M.F., Tarin, T.A., Al Mamun, M.R., Ruhad, F.M., Parven, A., Mubarak, N.M., Karri, S.L., Meftaul, I.M. (2023): Tea leaf disease detection and identification based on YOLOv7 (YOLO-T). Sci Rep, 13: 6078. DOI: 10.1038\/s41598-023-33270-4","DOI":"10.1038\/s41598-023-33270-4"},{"key":"2026061604590361186_j_helm-2026-0002_ref_031","doi-asserted-by":"crossref","unstructured":"Stark, T., Stefan, V., Wurm, M., Spanier, R., Taubenb\u00f6ck, H., Knight, T.M. (2023): YOLO object detection models can locate and classify broad groups of flower-visiting arthropods in images. Sci Rep, 13: 16364. DOI: 10.1038\/s41598-023-43482-3","DOI":"10.1038\/s41598-023-43482-3"},{"key":"2026061604590361186_j_helm-2026-0002_ref_032","unstructured":"Thorne, G., Allen, M.W. (1950): Pratylenchus hamatus n. sp. and Xiphinema index n. sp. two nematodes associated with fig roots with a note on Paratylenchus anceps Cobb. Proc Helminthol Soc Wash, 17: 27\u201335"},{"key":"2026061604590361186_j_helm-2026-0002_ref_033","doi-asserted-by":"crossref","unstructured":"Uygun, T., \u00d6zg\u00fcven, M.M., Yanar, D. (2024): Detection of damage caused by some vineyard pests with the YOLOv8x model using deep learning and object detection methods. Turk J Agric Food Sci Technol, 12(3): 386\u2013396. DOI: 10.24925\/turjaf.v12i3.386-396.6510","DOI":"10.24925\/turjaf.v12i3.386-396.6510"},{"key":"2026061604590361186_j_helm-2026-0002_ref_034","doi-asserted-by":"crossref","unstructured":"Wang, C., Sun, S., Zhao, C., Mao, Z., Wu, H., Teng, G. (2022): A detection model for cucumber root-knot nematodes based on modified YOLOv5-CMS. Agronomy, 12(10): 2555. DOI: 10.3390\/agronomy12102555","DOI":"10.3390\/agronomy12102555"},{"key":"2026061604590361186_j_helm-2026-0002_ref_035","doi-asserted-by":"crossref","unstructured":"Wang, C., Wang, L., Ma, G., Zhu, L. (2025): CSF-YOLO: A lightweight model for detecting grape leafhopper damage levels. Agronomy, 15(3): 741. DOI: 10.3390\/agronomy15030741","DOI":"10.3390\/agronomy15030741"},{"key":"2026061604590361186_j_helm-2026-0002_ref_036","doi-asserted-by":"crossref","unstructured":"Zaidi, S.S.A., Ansari, M.S., Aslam, A., Kanwal, N., Asghar, M., Lee, B. (2022): A survey of modern deep learning based object detection models. Digit Signal Process, 126: 103514. DOI: 10.1016\/j. dsp.2022.103514","DOI":"10.1016\/j.dsp.2022.103514"},{"key":"2026061604590361186_j_helm-2026-0002_ref_037","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Zhang, W., Li, R., Wang, J., Zhen, S., Niu, F. (2022): Improved YOLOv5-S object detection method for optical remote sensing images based on contextual transformer. J Electron Imaging, 31(4): 043049. DOI: 10.1117\/1.JEI.31.4.043049","DOI":"10.1117\/1.JEI.31.4.043049"}],"container-title":["Helminthologia"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/reference-global.com\/pdf\/10.2478\/helm-2026-0002","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:01:07Z","timestamp":1781586067000},"score":1,"resource":{"primary":{"URL":"https:\/\/reference-global.com\/article\/10.2478\/helm-2026-0002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,1]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,4,27]]},"published-print":{"date-parts":[[2026,3,1]]}},"alternative-id":["10.2478\/helm-2026-0002"],"URL":"https:\/\/doi.org\/10.2478\/helm-2026-0002","relation":{},"ISSN":["1336-9083"],"issn-type":[{"value":"1336-9083","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,1]]}}}