Related references
Note: Only part of the references are listed.Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform
Chengquan Zhou et al.
SENSORS (2019)
Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization
Xiong Xiong et al.
PLANT METHODS (2017)
Effect of rice panicle size on paddy field CH4 emissions
Yu Jiang et al.
BIOLOGY AND FERTILITY OF SOILS (2016)
In-field automatic observation of wheat heading stage using computer vision
Yanjun Zhu et al.
BIOSYSTEMS ENGINEERING (2016)
High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing
Fenner H. Holman et al.
REMOTE SENSING (2016)
Hyperspectral image analysis based on BoSW model for rice panicle blast grading
Shuangping Huang et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2015)
Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review
Sindhuja Sankaran et al.
EUROPEAN JOURNAL OF AGRONOMY (2015)
Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images
Wei Guo et al.
PLANT METHODS (2015)
Determination of rice panicle numbers during heading by multi-angle imaging
Lingfeng Duan et al.
CROP JOURNAL (2015)
Analysis of rice panicle traits and detection of QTLs using an image analyzing method
Mayuko Ikeda et al.
BREEDING SCIENCE (2010)
Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis
Zhan-Yu Liu et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2010)
The Markov chain Monte Carlo revolution
Persi Diaconis
BULLETIN OF THE AMERICAN MATHEMATICAL SOCIETY (2009)