标题
Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery
作者
关键词
-
出版物
Remote Sensing
Volume 10, Issue 6, Pages 950
出版商
MDPI AG
发表日期
2018-06-14
DOI
10.3390/rs10060950
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging
- (2018) Zohaib Khan et al. Plant Methods
- Land-based crop phenotyping by image analysis: consistent canopy characterization from inconsistent field illumination
- (2018) Joshua Chopin et al. Plant Methods
- Land-based crop phenotyping by image analysis: Accurate estimation of canopy height distributions using stereo images
- (2018) Jinhai Cai et al. PLoS One
- An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery
- (2018) Ana de Castro et al. Remote Sensing
- High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging
- (2018) Richard Makanza et al. Remote Sensing
- High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR
- (2018) Jose A. Jimenez-Berni et al. Frontiers in Plant Science
- Predicting plant biomass accumulation from image-derived parameters
- (2018) Dijun Chen et al. GigaScience
- Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring
- (2017) Nicolas Virlet et al. FUNCTIONAL PLANT BIOLOGY
- UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices
- (2017) Salvatore Filippo Di Gennaro et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System
- (2017) Jun Ni et al. SENSORS
- High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems
- (2017) et al. Remote Sensing
- Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)
- (2017) Friederike Gnädinger et al. Remote Sensing
- High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling
- (2017) Kakeru Watanabe et al. Frontiers in Plant Science
- Comparative Performance of Ground vs. Aerially Assessed RGB and Multispectral Indices for Early-Growth Evaluation of Maize Performance under Phosphorus Fertilization
- (2017) Adrian Gracia-Romero et al. Frontiers in Plant Science
- High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates
- (2017) Simon Madec et al. Frontiers in Plant Science
- Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality
- (2016) Troy S. Magney et al. AGRICULTURAL AND FOREST METEOROLOGY
- A survey of image processing techniques for plant extraction and segmentation in the field
- (2016) Esmael Hamuda et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?
- (2016) Jesper Rasmussen et al. EUROPEAN JOURNAL OF AGRONOMY
- Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries
- (2016) Atena Haghighattalab et al. Plant Methods
- Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery
- (2016) Michael Schirrmann et al. Remote Sensing
- High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing
- (2016) Fenner Holman et al. Remote Sensing
- A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization
- (2016) Omar Vergara-Díaz et al. Frontiers in Plant Science
- Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach
- (2015) Frank Liebisch et al. Plant Methods
- Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images
- (2015) Sebastian Candiago et al. Remote Sensing
- Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping
- (2014) David Deery et al. Agronomy-Basel
- A Flexible, Low-Cost Cart for Proximal Sensing
- (2013) Jeffrey W. White et al. CROP SCIENCE
- High-throughput phenotyping early plant vigour of winter wheat
- (2013) Sebastian Kipp et al. EUROPEAN JOURNAL OF AGRONOMY
- Development and evaluation of a field-based high-throughput phenotyping platform
- (2013) Pedro Andrade-Sanchez et al. FUNCTIONAL PLANT BIOLOGY
- Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps
- (2012) David J. Mulla BIOSYSTEMS ENGINEERING
- The application of small unmanned aerial systems for precision agriculture: a review
- (2012) Chunhua Zhang et al. PRECISION AGRICULTURE
- High-throughput non-destructive biomass determination during early plant development in maize under field conditions
- (2011) J.M. Montes et al. FIELD CROPS RESEARCH
- Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology
- (2010) Takeshi Motohka et al. Remote Sensing
- Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques
- (2010) Sudhanshu Sekhar Panda et al. Remote Sensing
- Estimation of leaf area index in cereal crops using red–green images
- (2009) Kristian Kirk et al. BIOSYSTEMS ENGINEERING
- Verification of color vegetation indices for automated crop imaging applications
- (2008) George E. Meyer et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic
- (2007) Bodo Mistele et al. FIELD CROPS RESEARCH
- Stereo Processing by Semiglobal Matching and Mutual Information
- (2007) H. Hirschmuller IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started