Joint crop and tassel segmentation in the wild

No abstract is available for this paper.

• Publication year

  2015

• Venue

  ACM Cloud and Autonomic Computing Conference

• Publication date

  2015-11-01

• Fields of study

  Agricultural and Food Sciences, Computer Science

• Identifiers
  DOI 10.1109/CAC.2015.7382547
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice

  2014cited by this paper
• Detecting corn tassels using computer vision and support vector machines

  2014cited by this paper
• Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage

  2013influential reference
• A Feature Learning Based Approach for Automated Fruit Yield Estimation

  2013cited by this paper
• An image-based approach for automatic detecting tasseling stage of maize using spatio-temporal saliency

  2013cited by this paper
• SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

  2012cited by this paper
• Definition of Linear Color Models in the RGB Vector Color Space to Detect Red Peaches in Orchard Images Taken under Natural Illumination

  2012influential reference
• Semantic Segmentation with Second-Order Pooling

  2012cited by this paper
• A multi-objective neural network based method for cover crop identification from remote sensed data

  2012cited by this paper
• An alternative method using digital cameras for continuous monitoring of crop status

  2012cited by this paper
• Automatic detection of crop rows in maize fields with high weeds pressure

  2012cited by this paper
• Corn tassel detection based on image processing

  2012cited by this paper
• Ensemble of exemplar-SVMs for object detection and beyond

  2011cited by this paper
• Automated rice leaf disease detection using color image analysis

  2011cited by this paper
• Type-2 fuzzy thresholding using GLSC histogram of human visual nonlinearity characteristics.

  2011cited by this paper
• Mean-shift-based color segmentation of images containing green vegetation

  2009cited by this paper
• Efficient Graph-Based Image Segmentation

  2004influential reference
• Crop growth estimation system using machine vision

  2003cited by this paper
• Machine-vision weed density estimation for real-time, outdoor lighting conditions

  1999cited by this paper
• Environmentally adaptive segmentation algorithm for outdoor image segmentation

  1998cited by this paper
• Color indices for weed identification under various soil, residue, and lighting conditions

  1994cited by this paper
• Estimates of genetic parameters for tosses characters in maize ( zea mays l ) and breeding respectives

  1985cited by this paper
• Leaf Angle, Tassel Morphology, and the Performance of Maize Hybrids1

  1978cited by this paper

• Deep Learning Based Stacking Ensembles for Tropical Sorghum Classification

  2025cites this paper
• Quantifying effect of tassels on near-ground maize canopy RGB images using deep learning segmentation algorithm

  2021influential citation
• Performance Evaluation of Crop Segmentation Algorithms

  2020cites this paper
• Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey

  2020cites this paper
• A contextualized approach for segmentation of foliage in different crop species

  2019cites this paper
• Region-based colour modelling for joint crop and maize tassel segmentation

  2016cites this paper