• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computers and Electronics in Agriculture >Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association
【24h】

Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association

机译：使用深神经网络和三维关联的葡萄检测，分割和跟踪

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Agricultural applications such as yield prediction, precision agriculture and automated harvesting need systems able to infer the crop state from low-cost sensing devices. Proximal sensing using affordable cameras combined with computer vision has seen a promising alternative, strengthened after the advent of convolutional neural networks (CNNs) as an alternative for challenging pattern recognition problems in natural images. Considering fruit growing monitoring and automation, a fundamental problem is the detection, segmentation and counting of individual fruits in orchards. Here we show that for wine grapes, a crop presenting large variability in shape, color, size and compactness, grape clusters can be successfully detected, segmented and tracked using state-of-the-art CNNs. In a test set containing 408 grape clusters from images taken on a trellis-system based vineyard, we have reached an F-1-score up to 0.91 for instance segmentation, a fine separation of each cluster from other structures in the image that allows a more accurate assessment of fruit size and shape. We have also shown as clusters can be identified and tracked along video sequences recording orchard rows. We also present a public dataset containing grape clusters properly annotated in 300 images and a novel annotation methodology for segmentation of complex objects in natural images. The presented pipeline for annotation, training, evaluation and tracking of agricultural patterns in images can be replicated for different crops and production systems. It can be employed in the development of sensing components for several agricultural and environmental applications.
机译：农业应用，如产量预测，精密农业和自动收割需求系统能够从低成本传感装置推断作物状态。使用经济实惠的相机与计算机视觉相结合的近端感应已经看过有希望的替代方案，在卷积神经网络（CNNS）的出现之后加强了自然图像中具有挑战性的模式识别问题的替代方案。考虑到果实日益增长的监测和自动化，基本问题是果园中单个果实的检测，分割和计数。在这里，我们展示了对于葡萄酒葡萄，可以使用最先进的CNN成功地检测到葡萄葡萄，颜色，尺寸和紧凑性，葡萄簇，葡萄簇。在包含基于格子系统的葡萄园上的图像的测试集中，我们已经达到了F-1-1-Integ分割，例如分割，从图像中的其他结构中的每个群集进行精细分离，允许a更准确地评估水果尺寸和形状。我们也显示出群体可以沿记录果园行的视频序列识别和跟踪群集。我们还提供了一个包含在300图像中正确注释的葡萄集群的公共数据集和用于分割自然图像中的复杂对象的新型注释方法。对于不同的作物和生产系统，可以复制图像的注释，培训，评估和跟踪图像中的农业模式的呈现管道。它可以用于开发用于几种农业和环境应用的传感部件。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号