Training instance segmentation neural network with synthetic datasets for crop seed phenotyping
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Training instance segmentation neural network with synthetic datasets for crop seed phenotyping
Authors
Keywords
-
Journal
Communications Biology
Volume 3, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-04-15
DOI
10.1038/s42003-020-0905-5
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Active learning with point supervision for cost-effective panicle detection in cereal crops
- (2020) Akshay L. Chandra et al. Plant Methods
- Single-Shot Convolution Neural Networks for Real-Time Fruit Detection Within the Tree
- (2019) Kushtrim Bresilla et al. Frontiers in Plant Science
- Using DeepLabCut for 3D markerless pose estimation across species and behaviors
- (2019) Tanmay Nath et al. Nature Protocols
- Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection
- (2019) Marko Arsenovic et al. Symmetry-Basel
- Deep learning in agriculture: A survey
- (2018) Andreas Kamilaris et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- An explainable deep machine vision framework for plant stress phenotyping
- (2018) Sambuddha Ghosal et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Control of grain size in rice
- (2018) Na Li et al. Plant Reproduction
- Aerial Imagery Analysis – Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy
- (2018) Wei Guo et al. Frontiers in Plant Science
- Fully Convolutional Networks for Semantic Segmentation
- (2017) Evan Shelhamer et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Multi-modal sliding window-based support vector regression for predicting plant water stress
- (2017) Yukimasa Kaneda et al. KNOWLEDGE-BASED SYSTEMS
- Extreme Suppression of Lateral Floret Development by a Single Amino Acid Change in the VRS1 Transcription Factor
- (2017) Shun Sakuma et al. PLANT PHYSIOLOGY
- A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images
- (2016) Nathan D. Miller et al. PLANT JOURNAL
- Characterization of Lr75: a partial, broad-spectrum leaf rust resistance gene in wheat
- (2016) Jyoti Singla et al. THEORETICAL AND APPLIED GENETICS
- Using Deep Learning for Image-Based Plant Disease Detection
- (2016) Sharada P. Mohanty et al. Frontiers in Plant Science
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Computer vision technology determines optimal physical parameters for sorting JinDan 73 maize seeds
- (2015) K.X. Wen et al. SEED SCIENCE AND TECHNOLOGY
- Comparison of digital image analysis using elliptic Fourier descriptors and major dimensions to phenotype seed shape in hexaploid wheat (Triticum aestivum L.)
- (2012) Keith Williams et al. EUPHYTICA
- SmartGrain: High-Throughput Phenotyping Software for Measuring Seed Shape through Image Analysis
- (2012) T. Tanabata et al. PLANT PHYSIOLOGY
- Rapid analysis of seed size in Arabidopsis for mutant and QTL discovery
- (2011) Rowan P Herridge et al. Plant Methods
- Separating parental environment from seed size effects on next generation growth and development in Arabidopsis
- (2010) ANGELA L. ELWELL et al. PLANT CELL AND ENVIRONMENT
- Natural variation of morphological traits in wild wheat progenitor Aegilops tauschii Coss.
- (2009) Shigeo Takumi et al. BREEDING SCIENCE
- The Pascal Visual Object Classes (VOC) Challenge
- (2009) Mark Everingham et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Genome-wide association study of grain shape variation among Oryza sativa L. germplasms based on elliptic Fourier analysis
- (2009) Hiroyoshi Iwata et al. MOLECULAR BREEDING
- Isolation and initial characterization of GW5, a major QTL associated with rice grain width and weight
- (2008) Jianfeng Weng et al. CELL RESEARCH
- Deletion in a gene associated with grain size increased yields during rice domestication
- (2008) Ayahiko Shomura et al. NATURE GENETICS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now