Abstract: A method and a system for automatically detecting nitrogen content, an electronic device and a medium are provided and relate to the field of nitrogen content detection. The method includes: selecting an optimal nitrogen content prediction model from a prediction model base according to a variety and a growth stage of a target crop to obtain a target model; detecting, based on a spectral image of the target crop, a nitrogen content of the target crop by using the target model to obtain a predicted value of the nitrogen content. The optimal nitrogen content prediction model is the model with best performance among a first nitrogen content prediction model constructed based on a hyperspectral vegetation index, a second nitrogen content prediction model constructed based on a sensitive band, and a third nitrogen content prediction model constructed based on a machine learning method.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure relates to a method and system for predicting a severity of bacterial blight of rice based on multi-phenotypic parameters. The method includes: calculating a rice spectral reflectance corresponding to each of the plots in a study area based on multi-spectral images, and screening characteristic variables based on a rice leaf chlorophyll content and a rice plant water content (WC) under stress of the bacterial blight to establish a new spectral index (SI); predicting the rice leaf chlorophyll content and the rice plant WC in the study area based on the rice spectral reflectance and regression model prediction, and predicting the incidence of the bacterial blight of rice; and obtaining quick indication of the severity of the bacterial blight of rice based on the new SI and the prediction. The method is suitable for high-throughput rice disease phenotype monitoring research.

Abstract: The present disclosure provides a fluorescent probe for detection of jasmonic acid (JA) and a preparation method thereof, and a detection method of JA, belonging to the technical field of component detection, The fluorescent probe for detection of JA includes a cobalt-based metal-organic framework (MOF) material, and aminated carbon quantum dots (CQDs) and a molecular imprinting polymer (MIP) that are distributed on a surface of the Co-MOF material; where the MIP has a molecular imprinting (MI) of the JA. In the present disclosure, the NCQDs are distributed on the surface of the Co-MOF material, and cobalt ions can undergo a coordination reaction with amino groups to change a charge distribution on a surface of the NCQD; meanwhile, the Co-MOF material can also avoid aggregation of the NCQDs to improve a detection stability.

Abstract: A method and system for screening spectral indexes of rice resistant to bacterial. 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.

Abstract: The present disclosure provides a ratiometric fluorescent probe, a preparation method thereof, and an application in detection of hydrogen peroxide. In the present disclosure, MoOx QDs (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 “anchors” for MoOx QDs, avoiding the aggregation of MoOx QDs and enhancing the stability of the probe.

Abstract: 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.

Abstract: The present disclosure relates to a method and system for the in-situ detection of cadmium chelates in a plant. The method includes: preparing cadmium chloride and phytochelatin (PC) 2 samples with different concentrations; using terahertz time-domain spectroscopy (THz-TDS) to determine a terahertz (THz) reflection image for the cadmium chloride and PC 2 samples; building a cadmium chloride content detection model according to first spectral data; building a PC 2 content detection model according to second spectral data; determining an optimal mixed sample according to the cadmium chloride content detection model and the PC 2 content detection model; building a Cd—PC2 content detection model according to the optimal mixed sample; and using the Cd—PC2 content detection model to determine a cadmium chelate content in a to-be-tested plant. The present disclosure can quickly and in-situ detect the heavy metal accumulation capacity of a plant.

Abstract: The present disclosure relates to a method and system for the in-situ detection of cadmium chelates in a plant. The method includes: preparing cadmium chloride and phytochelatin (PC) 2 samples with different concentrations; using terahertz time-domain spectroscopy (THz-TDS) to determine a terahertz (THz) reflection image for the cadmium chloride and PC 2 samples; building a cadmium chloride content detection model according to first spectral data; building a PC 2 content detection model according to second spectral data; determining an optimal mixed sample according to the cadmium chloride content detection model and the PC 2 content detection model; building a Cd—PC2 content detection model according to the optimal mixed sample; and using the Cd—PC2 content detection model to determine a cadmium chelate content in a to-be-tested plant. The present disclosure can quickly and in-situ detect the heavy metal accumulation capacity of a plant.