Nanjing Agricultural University

Tao Cheng

Yongchao Tian

Yan Zhu

Xia Yao

Hengbiao Zheng

Dong Zhou

Weixing Cao

Yu Zhang

Chongya Jiang

Caili Guo

Jifeng Ma

Hongxu Ai

Jie Zhu

Weixin Cao

Rui Huang

Tai Guo

Xiaohu Zhang

A field wheat stem tillering number extraction method, including: acquiring field wheat point clouds by means of a LiDAR, and extracting any row of wheat point clouds in a research area; projecting a Y axis to a plane, and retaining an X and Z axis; applying adaptive layering to obtain number of clusters of the wheat row; applying hierarchical clustering analysis to obtain tillering number of each wheat cluster; and further obtaining stem tillering number of the whole wheat row, so as to extract a field wheat stem tillering number. The feasibility of an algorithm is verified by comparing the wheat stem tillering number extracted by means of the method with an actually measured field stem tillering number, and the method realizes rapid, accurate and nondestructive extraction of a large-field crop stem tillering number and provides theoretical basis and technical support for extraction of the field wheat stem tillering number.

Method of extracting number of stem and tillering for wheat under field condition

A method for estimating the aboveground biomass of rice based on multi-spectral images of an unmanned aerial vehicle (UAV), including: normatively collecting UAV multi-spectral image data of rice canopy and ground measured biomass data; after collection, preprocessing images, extracting reflectivity and texture feature parameters, calculating a vegetation index, and constructing a new texture index; and by stepwise multiple regression analysis, integrating the vegetation index and the texture index to estimate rice biomass, and establishing a multivariate linear model for estimating biomass. A new estimation model is verified for accuracy by a cross-validation method. The method has high estimation accuracy and less requirements on input data, and is suitable for the whole growth period of rice. Estimating rice biomass by integrating UAV spectrum and texture information is proposed for the first time, and can be widely used for monitoring crop growth by UAV remote sensing.

Method for estimating aboveground biomass of rice based on multi-spectral images of unmanned aerial vehicle

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Nanjing, China

Tao Cheng