Scholarlink

Abstract <p>Some automated methods of sunflower seedling counting and weed vegetation mapping by unmanned aerial vehicle captured RGB images of ultrahigh spatial resolution are proposed. The methods are based on the application of convolutional DeepLabv3+, ResNet-101, and YOLOv8 neural networks. The results of experimental studies show that the sunflower seedling counting precision at early stages is averagingly 96<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\%\)</EquationSource> <!--OptelIns2570048Pestunov-m1--> </InlineEquation>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Sunflower Seedling Counting and Weed Detection by Deep Learning on Unmanned Aerial Vehicle Captured RGB Images

  • 期刊论文

  • 发表日期：

    2025年8月1日

查看全文
  • I. A. Pestunov,
  • R. A. Kalashnikov,
  • N. V. Ovcharova,
  • V. I. Belyaev,
  • M. M. Silantieva

摘要

Abstract

Some automated methods of sunflower seedling counting and weed vegetation mapping by unmanned aerial vehicle captured RGB images of ultrahigh spatial resolution are proposed. The methods are based on the application of convolutional DeepLabv3+, ResNet-101, and YOLOv8 neural networks. The results of experimental studies show that the sunflower seedling counting precision at early stages is averagingly 96 \(\%\) .