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: 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: 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.