Showing 81 - 100 results of 433 for search 'T36 (classification)', query time: 0.06s Refine Results
  1. 81
  2. 82
  3. 83

    A Lightweight Model for Shine Muscat Grape Detection in Complex Environments Based on the YOLOv8 Architecture by Changlei Tian, Zhanchong Liu, Haosen Chen, Fanglong Dong, Xiaoxiang Liu, Cong Lin

    Published 2025-01-01
    “…Evaluated on the newly developed Shine-Muscat-Complex dataset of 4715 images, the proposed model achieved a 2.6% improvement in mean Average Precision (mAP) over YOLOv8n while reducing parameters by 36.8%, FLOPs by 34.1%, and inference time by 15%. …”
    Get full text
    Article
  4. 84
  5. 85

    A Large-Scale Agricultural Land Classification Method Based on Synergistic Integration of Time Series Red-Edge Vegetation Index and Phenological Features by Huansan Zhao, Chunyan Chang, Zhuoran Wang, Gengxing Zhao

    Published 2025-01-01
    “…Agricultural land classification plays a pivotal role in food security and ecological sustainability, yet achieving accurate large-scale mapping remains challenging. …”
    Get full text
    Article
  6. 86
  7. 87
  8. 88
  9. 89

    Classification of risk for transmission of vaccine-preventable diseases in Brazilian municipalities: comparative analysis before and after the national movement for vaccination and... by Ana Catarina de Melo Araújo, Thales Philipe Rodrigues da Silva, Janaina Fonseca Almeida Souza, Krishna Mara Rodrigues Freire, Fernanda Penido Matozinhos, Eder Gatti Fernandes

    Published 2025-02-01
    “…Abstract Objective To analyze the classification of the risk of transmission of vaccine-preventable diseases in Brazilian municipalities before and after the National Movement for Vaccination and Multivaccination proposed by the Ministry of Health. …”
    Get full text
    Article
  10. 90
  11. 91
  12. 92
  13. 93
  14. 94
  15. 95
  16. 96
  17. 97

    Land Use and Land Cover Change Detection Using Remote Geospatial Techniques: A Case Study of an Urban City in Southwestern, Nigeria by Adenike Olayungbo

    Published 2021-06-01
    “…The results show that 26.36% of forest cover and 44.48% of waterbody were lost between the period of 1986 and 2018. …”
    Get full text
    Article
  18. 98
  19. 99
  20. 100