Convolutional neural network-based alien plant intrusion monitoring method and system

The invention discloses an alien plant intrusion monitoring method and system based on a convolutional neural network. The method comprises the following steps: shooting in a selected area according to a plurality of preset flight heights based on an unmanned aerial vehicle, and obtaining a target hyperspectral image; generating a training data set according to the waveband data of each pixel point in a specified region of interest in the target hyperspectral image; training and optimizing a preset convolutional neural network model according to the training data set to obtain a trained target convolutional neural network model; on-site verification is carried out based on each target convolutional neural network model, and an optimal convolutional neural network model with the highest recognition precision is determined; the to-be-recognized hyperspectral image is shot in the to-be-recognized area according to the optimal flight height based on the unmanned aerial vehicle, and the specified alien plant is re