Abstract
Timely treatment and elimination of diseases and pests can effectively improve the yield and quality of crops, but the current identification methods are difficult to achieve efficient and accurate research and analysis of diseases and pests. To solve this problem, this study proposes a crop pest identification method based on a multilayer network model. First, the method provides a reliable sample dataset for the recognition model through image data enhancement and other operations; the corresponding pest image recognition and analysis model is constructed based on VGG16 and Inception-ResNet-v2 transfer learning network to ensure the completeness of the recognition and analysis model; then, using the idea of an integrated algorithm, the two improved CNN series pest image recognition and analysis models are effectively fused to improve the accuracy of the model for crop pest recognition and classification. The simulation analysis is realized based on the IDADP dataset. The experimental results show that the accuracy of the proposed method for pest identification is 97.71%, which improves the poor identification effect of the current method.
Highlights
This study proposes a crop pest identification method based on an improved transfer learning network
The image analysis model based on the VGG16 network and Inception-ResNet-v2 network is constructed and fine-tuned to ensure the completeness of the image analysis model; at the same time, in order to improve the performance of the pest identification model, VGG16 and Inception-ResNet-v2 networks are effectively integrated by using the integrated algorithm to solve the problem of overfitting problem
The preprocessed pest dataset is used, and the image analysis network model is constructed based on VGG16 and Inception-ResNet-v2 network, and the image analysis model is further refined to ensure the completeness of the image analysis model. e statistical results of identification model indexes of diseases and pests show that the model in reference [20] cannot extract the characteristics of different types of diseases and pests well
Summary
As one of the most important industries in China, agriculture has become the foundation of the national economy. E quantity and quality of agricultural products are closely related to diseases and insect pests. Due to the incomplete dataset and the influence of the network’s structure, the depth network model has the overfitting problem, resulting in the low Computational Intelligence and Neuroscience accuracy of image recognition, which cannot meet the needs of efficient analysis of actual agricultural work scenes. To solve this problem, this study proposes a crop pest identification method based on an improved transfer learning network. The image analysis model based on the VGG16 network and Inception-ResNet-v2 network is constructed and fine-tuned to ensure the completeness of the image analysis model; at the same time, in order to improve the performance of the pest identification model, VGG16 and Inception-ResNet-v2 networks are effectively integrated by using the integrated algorithm to solve the problem of overfitting problem
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