MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization
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- Anil Bhujel & Na-Eun Kim & Elanchezhian Arulmozhi & Jayanta Kumar Basak & Hyeon-Tae Kim, 2022. "A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification," Agriculture, MDPI, vol. 12(2), pages 1-18, February.
- Haotian You & Yufang Lu & Haihua Tang, 2023. "Plant Disease Classification and Adversarial Attack Using SimAM-EfficientNet and GP-MI-FGSM," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
- Kamal KC & Zhendong Yin & Dasen Li & Zhilu Wu, 2021. "Impacts of Background Removal on Convolutional Neural Networks for Plant Disease Classification In-Situ," Agriculture, MDPI, vol. 11(9), pages 1-16, August.
- Ewa Ropelewska & Vanya Slavova & Kadir Sabanci & Muhammet Fatih Aslan & Veselina Masheva & Mariana Petkova, 2022. "Differentiation of Yeast-Inoculated and Uninoculated Tomatoes Using Fluorescence Spectroscopy Combined with Machine Learning," Agriculture, MDPI, vol. 12(11), pages 1-12, November.
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Keywords
tomato disease classification; out of distribution; corruption shifts generalization; frequency component; attention mechanism;All these keywords.
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