Patent ID: 11864552
Assignee: ZHEJIANG UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC A  G | IPC A  G

Claim 6:
7. A digital detection system for predicting drug resistance of transgenic maize, comprising:
a detection information acquisition module, configured to acquire detection information of a maize plant after medicament spraying at a current moment; wherein the detection information comprises an RGB image, three-dimensional point cloud data and a chlorophyll relative content;
a pixel ratio calculation module, configured to calculate a pixel ratio of the maize plant at the current moment according to the RGB image at the current moment; wherein the pixel ratio is a ratio of a pixel point number in a first region of the maize plant to a pixel point number in a second region of the maize plant; the first region of the maize plant is a leaf region of the maize plant that changes after the maize plant is treated by the medicament; and the second region is a region of all the leaves of the maize plant;
a morphological feature calculation module, configured to calculate a morphological feature of the maize plant at the current moment according to the three-dimensional point cloud data at the current moment; wherein the morphological feature comprises a plant height, a crown layer diameter, a stem thickness, a stem height, a first leaf length, a first leaf width, a second leaf length, a second leaf width, a third leaf length, and a third leaf width; the first leaf length, the second leaf length and the third leaf length are defined according to an order in which the root of the maize plant is upward and the branches and leaves are inward; and the first leaf width, the second leaf width and the third leaf width are defined according to an order in which the root of the maize plant is upward and the branches and leaves are inward;
a detection parameter and detection parameter changing graph prediction module, configured to input 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 of the maize plant in a next period; wherein the series model is constructed based on a convolutional neural network and a long-short term memory network; the detection parameter comprises the chlorophyll relative content, the pixel ratio, the plant height, the crown layer diameter, the stem thickness, the stem height, the first leaf length, the first leaf width, the second leaf length, the second leaf width, the third leaf length and the third leaf width; and the period is composed of a plurality of successive moments;
a drug resistance characteristic determination module, configured to estimate a resistance characteristic of the maize plant according to the graph of the change in the detection parameter of the maize plant; and
a plant variety prediction module, configured to input the detection parameter of the maize plant at the current moment into a parallel model to predict a variety of the maize plant; wherein the parallel model is constructed based on a convolutional neural network and a long-short term memory network.