An Enhanced Image Classification Model Based on Graph Classification and Superpixel-Derived CNN Features for Agricultural Datasets
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Abstract
Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches, leveraging the relational structure between image regions to improve accuracy. This paper presents an enhanced gr... | Find, read and cite all the research you need on Tech Science Press
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Research context
OpenAlex topics: E-commerce and Technology Innovations · Smart Agriculture and AI · Remote Sensing and Land Use
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References (41)
Show source-provided references
1. A survey on deep learning and its impact on agriculture: challenges and opportunities10.3390/agriculture13030540 ↗
2. Precision farming for sustainability: an agricultural intelligence model10.1016/j.compag.2024.109386 ↗
3. Plant disease detection and classification techniques: a comparative study of the performances10.1186/s40537-023-00863-9 ↗
4. Fruit ripeness classification: a survey
5. Transforming weed management in sustainable agriculture with artificial intelligence: a systematic literature review towards weed identification and deep learning10.1016/j.cropro.2023.106522 ↗
6. Deep learning models for plant disease detection and diagnosis10.1016/j.compag.2018.01.009 ↗
7. Distinctive image features from scale-invariant keypoints10.1023/B:VISI.0000029664.99615.94 ↗
8. Histograms of oriented gradients for human detection
9. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
10. Simonyan K. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. 2014. doi:10.48550/arXiv.1409.1556.
11. Going deeper with convolutions
12. Deep residual learning for image recognition
13. ImageNet: a large-scale hierarchical image database
14. Plant disease detection and classification using hybrid model based on convolutional auto encoder and convolutional neural network
15. A comprehensive survey on graph neural networks10.1109/TNNLS.2020.2978386 ↗
16. Graph neural networks: a review of methods and applications10.1016/j.aiopen.2021.01.001 ↗
17. Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907. 2016. doi:10.48550/arXiv.1609.02907.
18. Hamilton W, Ying Z, Leskovec J. Inductive representation learning on large graphs. arXiv:1706.02216. 2017. doi:10.48550/arXiv.1706.02216.
19. Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks? arXiv:1810.00826. 2018. doi:10.48550/arXiv.1810.00826.
20. Graph convolutional networks for image classification: comparing approaches for building graphs from images
21. Han K, Wang Y, Guo J, Tang Y, Wu E. Vision GNN: an image is worth graph of nodes. arXiv:2206.00272. 2022. doi:10.48550/arXiv.2206.00272.
22. Graph convolutional networks for hyperspectral image classification10.1109/TGRS.2020.3015157 ↗
23. Adaptive multi-feature fusion graph convolutional network for hyperspectral image classification10.3390/rs15235483 ↗
24. CNN-enhanced graph convolutional network with pixel- and superpixel-level feature fusion for hyperspectral image classification10.1109/TGRS.2020.3037361 ↗
25. Edwards M, Xie X. Graph based convolutional neural network. arXiv:1609.08965. 2016. doi:10.48550/arXiv.1609.08965.
26. Superpixels and graph convolutional neural networks for efficient detection of nutrient deficiency stress from aerial imagery
27. Image classification based on deep graph convolutional networks
28. Knyazev B, Lin X, Amer MR, Taylor GW. Image classification with hierarchical multigraph networks. arXiv:1907.09000. 2019. doi:10.48550/arXiv.1907.09000.
29. Wharton Z, Behera A, Bera A. An attention-driven hierarchical multi-scale representation for visual recognition. arXiv:2110.12178. 2021. doi:10.48550/arXiv.2110.12178.10.5244/C.35.411 ↗
30. Learning a classification model for segmentation
31. SLIC superpixels compared to state-of-the-art superpixel methods10.1109/TPAMI.2012.120 ↗
32. Deep learning10.1038/nature14539 ↗
33. Tan M, Le Q. EfficientNet: rethinking model scaling for convolutional neural networks. arXiv:1905.11946. 2019. doi:10.48550/arXiv.1905.11946.
34. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
35. Deeper insights into graph convolutional networks for semi-supervised learning
36. Rong Y, Huang W, Xu T, Huang J. Dropedge: towards deep graph convolutional networks on node classification. arXiv:1907.10903. 2019. doi:10.48550/arXiv.1907.10903.
37. Scholkemper M, Wu X, Jadbabaie A, Schaub MT. Residual connections and normalization can provably prevent oversmoothing in GNNs. arXiv:2406.02997. 2024. doi:10.48550/arXiv.2406.02997.
38. Wu Y. Attention is all you need for boosting graph convolutional neural network. arXiv:2403.15419. 2024. doi:10.48550/arXiv.2403.15419.
39. Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y. Graph attention networks. arXiv:1710.10903. 2017.
40. Tackling over-smoothing in multi-label image classification using graphical convolution neural network10.1007/s12530-022-09463-z ↗
41. Kaustubh B. “Kaggle” [Internet]. [cited 2025 Jan 10]. Available from: https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf.