scispace - formally typeset
C

Chunjiang Zhao

Researcher at Center for Information Technology

Publications -  279
Citations -  4923

Chunjiang Zhao is an academic researcher from Center for Information Technology. The author has contributed to research in topics: Computer science & Canopy. The author has an hindex of 30, co-authored 232 publications receiving 3384 citations. Previous affiliations of Chunjiang Zhao include Agricultural University of Hebei & Northeast Agricultural University.

Papers
More filters
Journal ArticleDOI

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

TL;DR: The current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed and can provide theoretical and technical support to promote the applications of Uav-R SPs for crop phenotypesing.
Journal ArticleDOI

Crop Phenomics: Current Status and Perspectives.

TL;DR: The challenges and prospective of crop phenomics are discussed in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.
Journal ArticleDOI

Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress

TL;DR: In this paper, the spectral reflectance of TM channel 5 derived from canopy spectra or image data at grain filling stage was all significantly correlated to grain protein content (R2 = 0.31 and 0.37, respectively).
Journal ArticleDOI

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

TL;DR: The results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass and may provide a guideline for improving the estimations of LAi and biomass of winter wheat using multisource remote sensing data.
Journal ArticleDOI

Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR).

TL;DR: In this paper, the applicability of the hyperspectral data from the canopy to the prediction of wheat grain quality was assessed for winter wheat, and the results indicated that the plant pigment ratio (PPR, (R550−R450)/(R550+R450)), a Chl-based index was most applicable to predict wheat grain protein due to its significant correlation with leaf N concentration at the post-anthesis stage.