Method and system for detecting Fusarium moniliforme species of rice seed

A system for detecting Fusarium moniliforme species of rice seed, comprising:

• a module for obtaining a hyperspectral image of a to-be-tested rice seed, configured to obtain the hyperspectral image of the to-be-tested rice seed;
• and
• a test result determining module, configured to input the hyperspectral image of the to-be-tested rice seed to a model for detecting Fusarium moniliforme species of rice seed, to determine a test result of the rice seeds, wherein the test result is selected from a group consisting of no Fusarium moniliforme and a Fusarium moniliforme species;
• wherein the model for detecting Fusarium moniliforme species of rice seed is determined based on activated wavelengths and an original deep convolutional neural network;
• the original deep convolutional neural network comprises a plurality of convolutional layers;
• a data set comprises a plurality of samples, and each of the samples comprises input data and a class;
• the input data is an average spectrum of the rice seeds, and the class is selected from a group consisting of no Fusarium moniliforme and a Fusarium moniliforme species;
• the data set is divided into a training set and a test set;
• and
• 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.