Abstract: A general-purpose crosstalk correction method for mosaic multispectral imaging crop growth sensing devices, including: obtaining uniform light source images in different bands in a stepped manner using the sensing device in combination with a tunable monochromatic light source system; searching for uniform light source images in the corresponding bands, and traversing and recording indices of pixels with maximum response values in macro-pixel areas of the images in the bands; and extracting, according to the indices of the pixels, response values of positions of the pixels corresponding to the images in the stepped bands, and drawing response average value curves of channels; and drawing a Gaussian response curve according to response value curves of the channels, multiplying a pseudoinverse matrix of data of the response values by a Gaussian data matrix to obtain a correction coefficient matrix, and eliminating data crosstalk between the bands of raw spectral images using the matrix.

Abstract: 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 ?; constructing a wheat LAI estimation model based on a spectral variable SATFNIR-RE2; performing preliminary screening on the LAI estimation model using cross validation; and then testing the screened model with independent data.

Abstract: A method for estimating a wheat leaf area index (LAI) to mitigate the impact of the leaf chlorophyll content (LCC) and a residue-soil background, including the following steps: step one, acquiring data; step two, calculating a residue-soil adjusted red edge difference index, including: a, calculating an existing REDVI on the basis of the wheat canopy spectrum; b, calculating an existing REDVI on the basis of the field background spectrum; and c, combining RE1 and R bands of the wheat canopy multispectral curve to construct RSARE; step three, constructing a wheat LAI estimation model: and step four, checking the wheat LAI estimation model. The method can simultaneously mitigate the impact of the residue-soil background and LCC in the LAI estimation process. Besides, the wheat LAI estimation model constructed on the basis of the index can estimate the LAI at an early stage in a wheat production process.

Abstract: A method for automatically estimating a leaf inclination angle distribution of a wheat canopy based on a voxel segmentation normal vector algorithm, including the following steps: step 1: obtaining point cloud data of a wheat canopy; step 2: splicing and denoising point clouds; step 3: calculating normal vectors of the point clouds; step 4: performing voxelization on the point clouds; step 5: segmenting the normal vectors by using a voxel; step 6: calculating angles of the voxels; step 7: collecting statistics on the angles of the voxels and performing curve fitting calculation to obtain a leaf inclination angle distribution and an average leaf inclination angle. The average leaf inclination angle estimated by the method is compared with field measured data, and the feasibility of the algorithm is verified by using a three-dimensional radiative transfer model.

Abstract: A handheld snapshot multispectral imaging crop growth sensing device, including an imaging objective lens, a spectral imaging module, a main control module, a power supply module, a stability maintenance gimbal, a Red-Green-Blue (RGB) imaging module, a ranging sensor, a handheld rod, a control display, and a housing. The imaging objective lens is arranged below the housing. The spectral imaging module is arranged above the imaging objective lens. The main control module is arranged above the spectral imaging module. The stability maintenance gimbal is arranged above the housing. The stability maintenance gimbal is connected to the handheld rod. The RGB imaging module and the ranging sensor are arranged below the housing. The control display is fastened to the handheld rod. The power supply module supplies power to various modules.

Abstract: 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.

Abstract: 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 ?; constructing a wheat LAI estimation model based on a spectral variable SATFNIR-RE2; performing preliminary screening on the LAI estimation model using cross validation; and then testing the screened model with independent data.

Abstract: 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.

Abstract: The present invention provides a BTK inhibitor, a pharmaceutically acceptable salt, a polymorph and an application thereof. Specifically, the present invention provides (R)-6-((1-acryloylpiperidin-3-yl)amino)-7-fluoro-4-((2-fluoro-4-morpholinophenyl)amino)-1,2-dihydro-3H-pyrrolo[3,4-c]pyridin-3-one, or the polymorph of a pharmaceutically acceptable salt thereof, and an application thereof. In addition, the present invention further discloses a pharmaceutical composition comprising the inhibitor and an application thereof.

Abstract: Provided are a CDK4/6 inhibitor, a pharmaceutically acceptable salt thereof and a polymorph thereof, and the use thereof. In particular, provided are a polymorph of 2-cyclopropyl-N-(5-((4-ethylpiperazin-1-yl)methyl)pyridin-2-yl)-3-isopropyl-3,8-dihydroimidazo[4?,5?,4,5]cyclopentadieno[1,2-d]pyrimidin-5-amine and a pharmaceutically acceptable salt thereof, and the use thereof. In addition, further disclosed are a pharmaceutical composition of the compound and the use thereof.

Abstract: The present invention provides an EZH2 inhibitor and pharmaceutically acceptable salts and polymorphic substances thereof, and application of the EZH2 inhibitor. Specifically, the present invention provides N-((4,6-dimethyl-2-oxo-1,2-dihydropyridin-3-yl)methyl)-5-ethyl-4-(ethyl((1S,4S)-4-(3-methoxyazetidin-1-yl)cyclohexyl)amino)-1-methyl-1H-indazole-6-carboxamide or polymorphic substances of pharmaceutically acceptable salts thereof, and application of N-((4,6-dimethyl-2-oxo-1,2-dihydropyridin-3-yl)methyl)-5-ethyl-4-(ethyl((1S,4S)-4-(3-methoxyazetidin-1-yl)cyclohexyl)amino)-1-methyl-1H-indazole-6-carboxamide. In addition, also disclosed are a pharmaceutical composition containing the inhibitor and application thereof.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A crop growth monitoring device based on a 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.