A deep learning-based method for classification, detection, and localization of weeds in turfgrass.

Journal: Pest management science
PMID: 35900854

Abstract

BACKGROUND: Precision spraying of synthetic herbicides can reduce herbicide input. Previous research demonstrated the effectiveness of using image classification neural networks for detecting weeds growing in turfgrass, but did not attempt to discriminate weed species and locate the weeds on the input images. The objectives of this research were to: (i) investigate the feasibility of training deep learning models using grid cells (subimages) to detect the location of weeds on the image by identifying whether or not the grid cells contain weeds; and (ii) evaluate DenseNet, EfficientNetV2, ResNet, RegNet and VGGNet to detect and discriminate multiple weed species growing in turfgrass (multi-classifier) and detect and discriminate weeds (regardless of weed species) and turfgrass (two-classifier).

Authors

  • Xiaojun Jin
    College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
  • Muthukumar Bagavathiannan
    Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, United States of America.
  • Patrick E McCullough
    Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, USA.
  • 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.

Keywords

External Resources

Popular Topics
Recent Journals