Semi-supervised learning methods for weed detection in turf.

Journal: Pest management science
PMID: 38265105

Abstract

BACKGROUND: Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time-consuming approach of manually labeling and processing large image data sets to develop deep neural networks for weed detection. This research introduces a novel semi-supervised learning (SSL) approach for detecting weeds in turf. The performance of SSL was compared with that of ResNet50, a fully supervised learning (FSL) method, in detecting and differentiating sub-images containing weeds from those containing only turfgrass.

Authors

  • Teng Liu
    National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Institute of Technology, Beijing, China.
  • Danlan Zhai
    Peking University Institute of Advanced Agricultural Sciences / Shandong Laboratory of Advanced Agricultural Sciences at Weifang, Weifang, China.
  • Feiyu He
    Department of Computer Science, Duke University, Durham, North Carolina, USA.
  • 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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