Zhejiang University

Advanced New Technologies Co., Ltd.

Other

Zhejiang Mariculture Research Institute

Hangzhou Peilan Aviation Technology Co., Ltd.

Alibaba Group Holding Limited

Yong He

Hui Fang

Xuping Feng

Pengcheng Nie

Liang Zhang

Tian Chen

Guolai Ma

Yufei Liu

Bei Liu

Yicong Fan

Lei Lin

Liwen He

Mingzhu Tao

Haiyan Cen

Han Mei

Jinnuo Zhang

Fei Liu

Yongqiang Shi

Zeqing Zhang

Xiaoli Li

Yidan Bao

Yue Yu

Feng Li

Jun Zhou

Lei Zhou

Chen Chen

Hui Zhang

Rui Yang

Guangwei He

Wenwen Kong

Jiangang Shen

Yonghai Zhao

Bingquan Chu

Han Guo

Jiaming Li

Xiya Zhang

Fengshi Ma

Qiang Wu

Wenkai Zhang

Jiangpeng Zhu

Li Zhou

Lei Bian

Fangfang Qu

Hangjian Chu

Xiaoyue Du

Hangyu Zhou

Guoquan Zeng

Zhengjun Qiu

Junqi Yu

Jianzhong Ma

Maocang Yan

Zhengyu Zheng

Nan Zhao

Chengyong Cai

Jinhua Lei

Shiwei Yu

Shupei Xiao

Wenna Zhang

Xiulin Bai

Qingguan Wu

Yinglei Zhao

Zhihong Ma

Na Wu

Disclosed is a flexible filter spectral camera. A flexible filter module includes N flexible filter units corresponding to N different wavebands, respectively, where N is a positive integer greater than or equal to 2. A CMOS (Complementary Metal Oxide Semiconductor) module is used to convert incident light into a digital signal. Any one of the N flexible filter units can be switched to a preset position to filter light emitted from a lens and transmit light at a corresponding waveband to the CMOS module. The preset position is between the lens and the CMOS module. An image processor is connected to the CMOS module and is used to convert the digital signal into an image. In accordance with the present disclosure, precision imaging of the lens is achieved, the overlapping accuracy and imaging quality of the images are improved, and meanwhile, the processing efficiency for overlapped images is improved.

Flexible filter spectral camera

A method and system for detectingspecies of rice seeds are provided, relating to the field of rapid quality detection of rice seeds. The method includes: inputting a hyperspectral image of to-be-tested rice seeds to a model for detectingspecies of rice seed, to determine a test result of the rice seeds, where the test result is noor aspecies. The model for detectingspecies of rice seed is determined based on activated wavelengths and an original deep convolutional neural network; the activated wavelengths are wavelengths activated by a trained deep convolutional neural network upon correct classification; and the trained deep convolutional neural network is a neural network obtained by training the original deep convolutional neural network based on the training set.

Method and system for detectingspecies of rice seed

A method for monitoring rice bacterial blight includes: obtaining a multi-spectral image, severities of the rice bacterial blight, and accumulated temperature data of a rice field at different growth stages; obtaining resistance of rice varieties to the bacterial blight; extracting a mean canopy spectral reflectance of each plot in the rice field; conducting regression of the severity of the rice bacterial blight using a convolutional neural network based on the mean canopy spectral reflectance and the severity of the rice bacterial blight, and outputting a depth spectrum feature; training a disease severity regression model with the accumulated temperature data, the depth spectrum feature, and the resistance to the bacterial blight for each plot as an input and the corresponding severity as an output; and monitoring a severity of the rice bacterial blight in a to-be-monitored rice field using the disease severity regression model.

Method and system for monitoring rice bacterial blight in field based on multi-source data

A method and system for screening spectral indexes of rice resistant to bacteria. The method includes: processing spectral data of a test sample by a threshold segmentation algorithm to obtain average spectral information of each spectral image and a proportion of lesions corresponding to each spectral image; training a deep learning algorithm model based on a self-attention mechanism by using the average spectral information of each spectral image and the proportion of the corresponding lesions to construct a regression model for evaluating an area of the lesions; determining an optimal band combination and a weight value corresponding to each band in the optimal band combination based on the regression model for evaluating the area of the lesions, and then determining the spectral indexes; and identifying differences between rice of different genotypes at different times of infection by using the spectral indexes, and screening rice varieties resistant to bacterial blight.

Method and system for screening spectral indexes of rice resistant to bacterial blight

Provided is a method and system for determining plant leaf surface roughness. The method includes: acquiring a plurality of continuously captured zoomed-in leaf images by using a zoom microscope image capture system; determining a feature match set according to the zoomed-in leaf images; removing de-noised images of which the number of feature matches in feature match set is less than a second set threshold, to obtain n screened images; combining the n screened images to obtain a combined grayscale image; and determining plant leaf surface roughness according to the combined grayscale image. In the present disclosure, first, a plurality of zoomed-in leaf images are directly acquired by the zoom microscope image capture system quickly and accurately; the zoomed-in leaf images are then screened and combined to form a combined grayscale image; finally, three-dimensional roughness of a plant leaf surface is determined quickly and accurately according to the combined grayscale image.

Method and system for determining plant leaf surface roughness

A digital detection method and system for predicting drug resistance of transgenic maize are disclosed. The method includes acquiring an RGB image, three-dimensional point cloud data and chlorophyll relative content of a maize plant after medicament spraying at a current moment; calculating a pixel ratio and morphological feature according to the RGB image and three-dimensional point cloud data; inputting a detection parameter of the maize plant at the current moment into a series model to predict the detection parameter of the maize plant at a next moment to obtain a graph of change in the detection parameter in a next period; estimating a drug resistance characteristic according to the graph of the change in the detection parameter of the maize plant; and inputting the detection parameter of the maize plant at the current moment into a parallel model to predict the variety of the maize plant.

Digital detection method and system for predicting drug resistance of transgenic maize

It is provided a recognition device for analyzing a starvation extent of a whiteleg shrimp based on underwater imaging, including a bracket, a camera mounted on a top of the bracket, a plurality of light sources for illumination, and a processor connected with the camera. The processor receives a shrimp image collected by the camera, extracts an edge image of the shrimp after the collected shrimp image is preprocessed, and calculates a movement speed of the shrimp and a quantity of baits after a head and a tail are recognized, to recognize starvation extent of the shrimp. It is also provided a recognition method for analyzing starvation extent of the whiteleg shrimp based on underwater imaging. The present disclosure may guide feeders to feed and realize reasonable culture for the shrimp, by capturing images of the shrimp in time and determining starvation the extent of shrimp.

Recognition method and device for analyzing a starvation extent of a whiteleg shrimp based on underwater imaging

The present disclosure provides a stitching quality evaluation method and system, and a redundancy reduction method and system for low-altitude UAV remote sensing images, and belongs to the technical field of image processing. The method comprises: firstly acquiring ground images using a UAV under a preset overlap degree to obtain a low-altitude UAV remote sensing image set, then stitching the low-altitude UAV remote sensing image set to obtain a stitched image, and finally performing a quality evaluation on the stitched image using an improved BRISQUE algorithm to obtain an image quality score, which is applicable to quality evaluation of visible images and multispectral images at the same time through the improved BRISQUE algorithm. In addition, the present disclosure further provides an image redundancy reduction method based on the improved BRISQUE algorithm, thereby improving the image stitching efficiency and stitching quality.

Stitching quality evaluation method and system and redundancy reduction method and system for low-altitude unmanned aerial vehicle remote sensing images

Disclosed a crop yield prediction method and system based on low-altitude remote sensing information from an unmanned aerial vehicle (UAV). Obtaining a plurality of images taken by the UAV; stitching the plurality of images to obtain a stitched image; performing spectral calibration on the stitched image to obtain the reflectivity of each pixel in the stitched image; using a threshold segmentation method to segment the stitched image, to obtain a target area for crop yield prediction; using a Pearson correlation analysis method to analyze a correlation between the reflectivity of each band and the growth status and yield of the crop to obtain feature bands; constructing yield prediction factors based on the feature bands; and determining a predicted crop yield value of the target area for crop yield prediction based on the yield prediction factors and a crop planting area of the target area for crop yield prediction.

Crop yield prediction method and system based on low-altitude remote sensing information from unmanned aerial vehicle

Aerodynamic aquatic weed removal and decontamination devices and decontamination methods are disclosed. In an embodiment, the aerodynamic aquatic weed removal and decontamination device includes a boat body and a shore-based device for remotely controlling the boat body. The boat body includes a power unit, a weed removal and decontamination unit, a positioning system, an environment perception system, and a shipborne controller. The power unit includes an engine, a propeller connected to the engine, and a rudder servo motor configured to control a heading of the boat body. The weed removal and decontamination unit includes a mesh conveyor. The positioning system includes a positioner configured to obtain real-time location information and heading information of the boat body. The environment perception system includes a wind sensor and a visual sensor. The shipborne controller is configured to control a navigation state of the boat body.

Aerodynamic aquatic weed removal and decontamination device and decontamination method

A method and system for determining flight height of an unmanned aerial vehicle (UAV) includes a determining device that includes a camera at bottom of the UAV carried on a three-axis self-stabilizing tripod head; and a carrier phase difference satellite positioning system (CPDSPS), a graphics processing computer (GPC) and a power supply system provided on top of the UAV The GPC is connected with the CPDSPS, the power supply system and the camera, respectively. An attitude and heading reference system (AHRS) is provided at the bottom of the three-axis self-stabilizing tripod head and connected with the GPC The GPC is configured to determine relative height of the UAV from a canopy of farmland surface crops according to position information acquired by the CPDSPS, attitude information acquired by the AHRS and ground orthographic image acquired by the camera, and determine flight height of the UAV according to the relative height.

Device, method and system for determining flight height of unmanned aerial vehicle

An automatic feeding device and method for a seedling bed of a transplanter, comprising: a bracket installed on the transplanter; several trays, obliquely arranged on the bracket downwards, and the bottom of the trays being provided with release mechanisms for releasing the rice seedling bed; a conveyor belt, arranged on the bracket; a photoelectric sensor, arranged on the transplanting platform; and a controller, in communication connection with the photoelectric sensor, when the photoelectric sensor detects that the height of the remaining rice seedling bed on the transplanting platform is lower than the height of the photoelectric sensor. Only one driver is needed to operate, so that the labor input is greatly reduced, and the labor intensity is reduced; meanwhile, the space is saved by the reduction of the number of workers, enabling more seedling raw materials to be stored.

Automatic feeding device and method for seedling bed of transplanter

The present disclosure provides a ratiometric fluorescent probe, a preparation method thereof, and an application in detection of hydrogen peroxide. In the present disclosure, MoOQDs (nanoenzymes) and Co/Zn-MOFs both have catalytic activity, and the large specific surface area and porous structure of Co/Zn-MOFs can provide more binding sites for the contact between nanoenzymes and substrates. Moreover, Co/Zn-MOFs have high catalytic activity similar to natural enzymes. When nanoenzymes with fluorescent properties encounter Co/Zn-MOFs with similar catalytic activity, they will collide with a spark of 'synergy catalysis', and the fusion of the two plays a role of synergy catalysis; in addition, the uniform cavity of Co/Zn-MOFs can provide 'hosts' for nanoenzymes, and Co/Zn-MOFs provide &#x201c;anchors&#x201d; for MoOQDs, avoiding the aggregation of MoOQDs and enhancing the stability of the probe.

Ratiometric fluorescent probe, preparation method thereof, and application in detection of hydrogen peroxide

The present disclosure provides an intelligent replacement device and method for a trap board in a tea garden based on image channel computation. The device includes a trap board supply module, a trap board sticker removal module and a machine vision control module fixed on a machine frame, where the machine vision control module is configured to acquire an image of a present trap board in the trap board supply module, determine whether the present trap board fails according to the image, control, in response to failure of the present trap board, the trap board supply module to replace the trap board, and control the trap board sticker removal module to remove a film of a replaced trap board. The present disclosure can replace the trap board in the tea garden automatically, timely and accurately instead of the manual operation.

Intelligent replacement device and method for trap board in tea garden based on image channel computation

A method for rapid and quantitative determination of sildenafil in cocktail includes the exemplary steps of: Step 1, assigning a Raman spectrum of sildenafil; Step 2, weighing and dissolving a sildenafil standard in methanol to obtain a 1,000 mg/L sildenafil standard solution, and mixing cocktails with the sildenafil standard solution, to prepare test solutions; Step 3, mixing 100 &#x3bc;L each of test solutions with 500 &#x3bc;L of surface enhancer OTR 202 to acquire Raman spectra; and Step 4, establishing a linear regression model, with a formula of y=4035.1 x+503.26, where y is the content of sildenafil in cocktail, in mg/L, and x is intensity of Raman characteristic peaks at 1584 cm. Rapid determination of sildenafil by SERS is feasible and reliable, serving as a novel, rapid, and accurate method for quantitative determination of sildenafil in cocktail. The method meets the requirements of analysis and detection of sildenafil in other alcoholic beverages.

Method for rapid and quantitative determination of sildenafil in cocktail

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Hangzhou, China

Yong He