Nanjing Agricultural University

Xia Yao

Yongchao Tian

Yan Zhu

Weixing Cao

Hengbiao Zheng

Tao Cheng

Jun Ni

Yu Zhang

Fangrong Pang

Lili Yao

Fang Liu

Jifeng Ma

Caili Guo

Wei Li

Dong Zhou

Hongxu Ai

Jie Zhu

Rui Huang

Shanshan Yu

Xiaohu Zhang

Chongya Jiang

Dong Li

Xue Wang

Tai Guo

Weixin Cao

A method for improving estimation of leaf area index in an early growth stage of wheat based on red-edge bands of Sentinel-2 satellite images includes the following steps: acquiring field background spectrum and wheat canopy spectrum information by means of Sentinel-2; respectively acquiring a 'straw-soil' spectrum and a “wheat-straw-soil” spectrum, and calculating slopes of red-edge areas of the “straw-soil” spectrum and the “wheat-straw-soil” spectrum; calculating a tangent function SATF corrected by a background adjustment coefficient α; constructing a wheat LAI estimation model based on a spectral variable SATF; performing preliminary screening on the LAI estimation model using cross validation; and then testing the screened model with independent data.

Method for improving estimation of leaf area index in early growth stage of wheat based on red-edge band of sentinel-2 satellite image

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

A crop growth monitoring device based on multi-rotor unmanned aerial vehicle platform includes a multi-rotor unmanned aerial vehicle, a payload and a ground receiver; the payload part includes a multispectral crop growth sensor module, a signal amplification module, a controller module, a wireless data transmission module, and a power module for power supply and a power supply control module, which are connected in sequence; further comprising a gimbal. The crop growth monitoring device based on the multi-rotor unmanned aerial vehicle platform overcomes the influence on measurement caused by the down-wash flow field of the unmanned aerial vehicle, and can transmit the measured data to the ground receiver in real time for online analysis and processing, realizing continuous, real-time, convenient and large-scope acquisition of crop growth information.

Multi-rotor wing unmanned aerial vehicle platform based crop growth monitoring method and device

A crop growth sensing apparatus and method supporting agricultural machinery variable-quantity fertilization, includes a shock-absorbing support, a shock-absorbing frame, and a cantilever beam. The shock-absorbing support is connected to an agricultural machinery vehicle frame. The cantilever beam is pivotally connected above a shock-absorbing spring of the shock-absorbing support. The cantilever beam, by means shock-absorbing springs mounted thereabove and thereunder, is pivotally connected to the shock-absorbing frame. The shock-absorbing frame is fixedly connected to a crop growth sensor. The crop growth sensing method employs an engine vibration model of the agricultural machinery and a vibration model for vehicle wheels and farmland road surface as excitations in analyzing vibrations of a crop growth multispectral sensor shock-absorbing apparatus to determine the number and mount positions of sensitive elements of the crop growth multispectral sensor. Also provided are a self-balancing apparatus and a self-balancing method for the crop growth multispectral sensor.

Crop growth sensing apparatus and method supporting agricultural machinery variable-quantity fertilization operations

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Nanjing, China

Xia Yao