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Journal Abstract Search

132 related items for PubMed ID: 37836956

  • 1. A Deep Learning Approach for Precision Viticulture, Assessing Grape Maturity via YOLOv7.
    Badeka E, Karapatzak E, Karampatea A, Bouloumpasi E, Kalathas I, Lytridis C, Tziolas E, Tsakalidou VN, Kaburlasos VG.
    Sensors (Basel); 2023 Sep 27; 23(19):. PubMed ID: 37836956
    [Abstract] [Full Text] [Related]

  • 2. An Innovative Deep Learning Approach to Spinal Fracture Detection in CT Images.
    Wu H, Fu Q.
    Ann Ital Chir; 2024 Sep 27; 95(4):657-668. PubMed ID: 39186337
    [Abstract] [Full Text] [Related]

  • 3. wGrapeUNIPD-DL: An open dataset for white grape bunch detection.
    Sozzi M, Cantalamessa S, Cogato A, Kayad A, Marinello F.
    Data Brief; 2022 Aug 27; 43():108466. PubMed ID: 35873279
    [Abstract] [Full Text] [Related]

  • 4. Deep Learning Techniques for Grape Plant Species Identification in Natural Images.
    Pereira CS, Morais R, Reis MJCS.
    Sensors (Basel); 2019 Nov 07; 19(22):. PubMed ID: 31703313
    [Abstract] [Full Text] [Related]

  • 5. Developing an automatic warning system for anomalous chicken dispersion and movement using deep learning and machine learning.
    Chen BL, Cheng TH, Huang YC, Hsieh YL, Hsu HC, Lu CY, Huang MH, Nien SY, Kuo YF.
    Poult Sci; 2023 Dec 07; 102(12):103040. PubMed ID: 37769488
    [Abstract] [Full Text] [Related]

  • 6. Fruit Detection and Pose Estimation for Grape Cluster-Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks.
    Yin W, Wen H, Ning Z, Ye J, Dong Z, Luo L.
    Front Robot AI; 2021 Dec 07; 8():626989. PubMed ID: 34239899
    [Abstract] [Full Text] [Related]

  • 7. In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment.
    Ghiani L, Sassu A, Palumbo F, Mercenaro L, Gambella F.
    Sensors (Basel); 2021 Jun 05; 21(11):. PubMed ID: 34198844
    [Abstract] [Full Text] [Related]

  • 8. Comparing State-of-the-Art Deep Learning Algorithms for the Automated Detection and Tracking of Black Cattle.
    Myat Noe S, Zin TT, Tin P, Kobayashi I.
    Sensors (Basel); 2023 Jan 03; 23(1):. PubMed ID: 36617130
    [Abstract] [Full Text] [Related]

  • 9. A multi-source data fusion approach to assess spatial-temporal variability and delineate homogeneous zones: A use case in a table grape vineyard in Greece.
    Anastasiou E, Castrignanò A, Arvanitis K, Fountas S.
    Sci Total Environ; 2019 Sep 20; 684():155-163. PubMed ID: 31153064
    [Abstract] [Full Text] [Related]

  • 10. GrapesNet: Indian RGB & RGB-D vineyard image datasets for deep learning applications.
    Barbole DK, Jadhav PM.
    Data Brief; 2023 Jun 20; 48():109100. PubMed ID: 37089206
    [Abstract] [Full Text] [Related]

  • 11. Changes in Red Wine Composition during Bottle Aging: Impacts of Grape Variety, Vineyard Location, Maturity, and Oxygen Availability during Aging.
    Zhang X, Kontoudakis N, Šuklje K, Antalick G, Blackman JW, Rutledge DN, Schmidtke LM, Clark AC.
    J Agric Food Chem; 2020 Nov 25; 68(47):13331-13343. PubMed ID: 32066244
    [Abstract] [Full Text] [Related]

  • 12. MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion.
    Peng J, Ouyang C, Peng H, Hu W, Wang Y, Jiang P.
    Sensors (Basel); 2024 May 06; 24(9):. PubMed ID: 38733058
    [Abstract] [Full Text] [Related]

  • 13. On-The-Go VIS + SW - NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard.
    Fernández-Novales J, Tardáguila J, Gutiérrez S, Paz Diago M.
    Molecules; 2019 Jul 31; 24(15):. PubMed ID: 31370313
    [Abstract] [Full Text] [Related]

  • 14. Estimation of Sugar Content in Wine Grapes via In Situ VNIR-SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques.
    Kalopesa E, Karyotis K, Tziolas N, Tsakiridis N, Samarinas N, Zalidis G.
    Sensors (Basel); 2023 Jan 17; 23(3):. PubMed ID: 36772104
    [Abstract] [Full Text] [Related]

  • 15. Dataset on UAV RGB videos acquired over a vineyard including bunch labels for object detection and tracking.
    Ariza-Sentís M, Vélez S, Valente J.
    Data Brief; 2023 Feb 17; 46():108848. PubMed ID: 36619256
    [Abstract] [Full Text] [Related]

  • 16. Ethephon foliar application: Impact on the phenolic and technological Tempranillo grapes maturity.
    López R, Portu J, González-Arenzana L, Garijo P, Gutiérrez AR, Santamaría P.
    J Food Sci; 2021 Mar 17; 86(3):803-812. PubMed ID: 33590528
    [Abstract] [Full Text] [Related]

  • 17. Intelligent Detection Method for Wildlife Based on Deep Learning.
    Li S, Zhang H, Xu F.
    Sensors (Basel); 2023 Dec 07; 23(24):. PubMed ID: 38139515
    [Abstract] [Full Text] [Related]

  • 18. Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection algorithm.
    Zhou X, Zou X, Tang W, Yan Z, Meng H, Luo X.
    Front Plant Sci; 2023 Dec 07; 14():1103276. PubMed ID: 37332733
    [Abstract] [Full Text] [Related]

  • 19. Fluorescence-Sensor Mapping for the in Vineyard Non-Destructive Assessment of Crimson Seedless Table Grape Quality.
    Tuccio L, Cavigli L, Rossi F, Dichala O, Katsogiannos F, Kalfas I, Agati G.
    Sensors (Basel); 2020 Feb 12; 20(4):. PubMed ID: 32059448
    [Abstract] [Full Text] [Related]

  • 20. Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once.
    Adibhatla VA, Chih HC, Hsu CC, Cheng J, Abbod MF, Shieh JS.
    Math Biosci Eng; 2021 May 21; 18(4):4411-4428. PubMed ID: 34198445
    [Abstract] [Full Text] [Related]

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