Identification of Angelica sinensis diseases and insect pests based on the fu-sion of multiple convolutional neural networks
Aiming at the lack of identification methods for diseases and insect pests in Angelica sinensis industry,the subjective factors in the process of artificial feature extraction and the large amount of data required for training convolution-al neural networks,a method for identification of diseases and insect pests of Angelica sinensis based on the fusion of multi-ple convolutional neural networks was proposed.The dataset of common diseases and insect pests of Angelica sinensis was constructed.Four networks,ResNet50,InceptionNetV3,VGG19 and DenseNet201,with the best performance on the data-set of Angelica sinensis were selected as the base learner for model fusion.The XGBoost(extreme gradient boosting)algo-rithm was used as a meta-learner to obtain the Angelica sinensis diseases and insect pests recognition model based on the fu-sion of multiple convolutional neural networks.The results showed that the fusion model had higher recognition accuracy than a single convolutional neural network model,and was superior to other fusion models.The precision rate,recall rate and F1 value of the identification of Angelica sinensis pests and diseases reached 98.33%,97.14%and 97.68%,respective-ly.The model based on XGBoost fusion method proposed in this study realized the accurate classification of common disea-ses and insect pests of Angelica sinensis,and the identification accuracy rate of common diseases reached 98.33%,which provided an effective method for identification of diseases and insect pests in the industry of Angelica sinensis.
classification of Angelica sinensis dis-eases and insect pestsconvolutional neural networkex-treme gradient boosting(XGBoost)fusion method
万方数据
维普
