ArticleComputational intelligence and neuroscience2022
Improved CNN Method for Crop Pest Identification Based on Transfer Learning.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- TSSC: a new deep learning model for accurate pea leaf disease identification.Frontiers in plant science · 2025Article
- Peanut leaf disease identification with deep learning algorithms.Molecular breeding : new strategies in plant improvement · 2023Article
- Rice pest identification based on multi-scale double-branch GAN-ResNet.Frontiers in plant science · 2023Article
- A new model based on improved VGG16 for corn weed identification.Frontiers in plant science · 2023Article
- A lightweight model for efficient identification of plant diseases and pests based on deep learning.Frontiers in plant science · 2023Article
- Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis.Computational intelligence and neuroscience · 2022Article
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5 authors.
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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.
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