ArticleFrontiers in plant science2022
Crop Pest Recognition in Real Agricultural Environment Using Convolutional Neural Networks by a Parallel Attention Mechanism.
Article in Frontiers in plant science, 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.
- Phytomycobiomes and Ecosystem Services: Mechanisms, Evidence and Routes to Application.Journal of fungi (Basel, Switzerland) · 2025Review
- A CNN-Transformer Hybrid Framework for Multi-Label Predator-Prey Detection in Agricultural Fields.Sensors (Basel, Switzerland) · 2025Article
- ConvTransNet-S: A CNN-Transformer Hybrid Disease Recognition Model for Complex Field Environments.Plants (Basel, Switzerland) · 2025Article
- A Deep Learning-Based Model Approach for Quantitative Analysis of Cell Chemotaxis in a Microfluidic Chip.Sensors (Basel, Switzerland) · 2025Article
- A method for detecting the quality of cotton seeds based on an improved ResNet50 model.PloS one · 2023Article
- An Industrial-Grade Solution for Crop Disease Image Detection Tasks.Frontiers in plant science · 2022Article
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5 authors.
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Abstract
Crop pests are a major agricultural problem worldwide because the severity and extent of their occurrence threaten crop yield. However, traditional pest image segmentation methods are limited, ineffective and time-consuming, which causes difficulty in their promotion and application. Deep learning methods have become the main methods to address the technical challenges related to pest recognition. We propose an improved deep convolution neural network to better recognize crop pests in a real agricultural environment. The proposed network includes parallel attention mechanism module and residual blocks, and it has significant advantages in terms of accuracy and real-time performance compared with other models. Extensive comparative experiment results show that the proposed model achieves up to 98.17% accuracy for crop pest images. Moreover, the proposed method also achieves a better performance on the other public dataset. This study has the potential to be applied in real-world applications and further motivate research on pest recognition.
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