ArticleComputational intelligence and neuroscience2022
A Graph-Related High-Order Neural Network Architecture via Feature Aggregation Enhancement for Identification Application of Diseases and Pests.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- Residual swin transformer for classifying the types of cotton pests in complex background.Frontiers in plant science · 2024Article
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- Miniature Noninvasive Sensor Based on Impedance-Change Detection in Branches for Measuring Branch Ice Content in Overwintering Woody Plants.Micromachines · 2023Article
- Research on Rapid and Low-Cost Spectral Device for the Estimation of the Quality Attributes of Tea Tree Leaves.Sensors (Basel, Switzerland) · 2023Article
- Smart Soil Water Sensor with Soil Impedance Detected via Edge Electromagnetic Field Induction.Micromachines · 2022Article
- Prediction of Safety Risk Levels of Veterinary Drug Residues in Freshwater Products in China Based on Transformer.Foods (Basel, Switzerland) · 2022Article
- Construction of Digital Platform of Religious and Cultural Resources Using Deep Learning and Its Big Data Analysis.Computational intelligence and neuroscience · 2022Article
- The Collection and Utilization of Web Resources for Teaching World History Based on Data Mining Technology.Journal of environmental and public health · 2022Article
- The Spread, Rise, and Fall of University Students' Interconnected Internet Public Opinion in the Age of Big Data.Journal of environmental and public health · 2022Article
- Digital Construction of Vocal Music Teaching Resource Base Using Data Mining Technology.Journal of environmental and public health · 2022Article
- Monitoring and Sharing of Music Teaching Environment Resources Using Big Data Technology.Journal of environmental and public health · 2022Article
- Optimization Algorithm of Urban Rail Transit Network Route Planning Using Deep Learning Technology.Computational intelligence and neuroscience · 2022Article
- Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks.Journal of environmental and public health · 2022Article
- Application of SDN Network Traffic Prediction Based on Speech Recognition in Educational Information Optimization Platform.Computational intelligence and neuroscience · 2022Article
- Image Layout and Schema Analysis of Chinese Traditional Woodblock Prints Based on Texture and Color Texture Characteristics in the Environment of Few Samples.Computational intelligence and neuroscience · 2022Article
- Optimization Model of Mathematics Instructional Mode Based on Deep Learning Algorithm.Computational intelligence and neuroscience · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diseases and pests are essential threat factors that affect agricultural production, food security supply, and ecological plant diversity. However, the accurate recognition of various diseases and pests is still challenging for existing advanced information and intelligence technologies. Disease and pest recognition is typically a fine-grained visual classification problem, which is easy to confuse the traditional coarse-grained methods due to the external similarity between different categories and the significant differences among each subsample of the same category. Toward this end, this paper proposes an effective graph-related high-order network with feature aggregation enhancement (GHA-Net) to handle the fine-grained image recognition of plant pests and diseases. In our approach, an improved CSP-stage backbone network is first formed to offer massive channel-shuffled features in multiple granularities. Secondly, relying on the multilevel attention mechanism, the feature aggregation enhancement module is designed to exploit distinguishable fine-grained features representing different discriminating parts. Meanwhile, the graphic convolution module is constructed to analyse the graph-correlated representation of part-specific interrelationships by regularizing semantic features into the high-order tensor space. With the collaborative learning of three modules, our approach can grasp the robust contextual details of diseases and pests for better fine-grained identification. Extensive experiments on several public fine-grained disease and pest datasets demonstrate that the proposed GHA-Net achieves better performances in accuracy and efficiency surpassing several other existing models and is more suitable for fine-grained identification applications in complex scenes.
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