Detection and coverage estimation of purple nutsedge in turf with image classification neural networks.

Journal: Pest management science
PMID: 38436512

Abstract

BACKGROUND: Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image-classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf.

Authors

  • Xiaojun Jin
    College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
  • Kang Han
    Peking University Institute of Advanced Agricultural Sciences/Shandong Laboratory of Advanced Agricultural Sciences at Weifang, Weifang, China.
  • Hua Zhao
    Animal Nutrition Institute, Sichuan Agricultural University, Ya'an, Sichuan 625014, China.
  • Yan Wang
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
  • Yong Chen
    Department of Urology, Chongqing University Fuling Hospital, Chongqing, China.
  • Jialin Yu
    Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang 330031, China.

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