Method for intelligent identification of plant diseases and pests in landscape gardens based on multispectral images

Abstract

The present invention relates to a method for intelligent identification of plant diseases and pests in landscape gardens based on multispectral images, comprising: collecting multispectral images of landscape garden plants under different weather conditions, inputting the multispectral images into a key feature band extraction model to obtain key feature bands; the key feature band extraction model is obtained by training a deep learning model using a training set; the training set includes: original multispectral images; utilizing a multimodal classification model to perform feature extraction and classification decision-making on the key feature bands to obtain disease classification results; wherein, the multimodal classification model includes a maximum likelihood probability classifier and a depthwise separable convolutional neural network. The present invention significantly improves the model's generalization ability and recognition accuracy, achieving high-precision disease classification decision-making.