Data augmentation using improved cDCGAN for plant vigor rating

Author:

Zhu Fengle,He Mengzhu,Zheng Zengwei

Funder

Natural Science Foundation of Zhejiang Province

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference35 articles.

1. Fine-grained hierarchical classification of plant leaf images using fusion of deep models;Araujo,2018

2. Arjovsky, M., Chintala, S., Bottou, L., 2017. Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning. pp. 214–223.

3. CVAE-GAN: fine-grained image generation through asymmetric training;Bao,2017

4. Bowles, C., Chen, L., Guerrero, R., Bentley, P., Gunn, R., Hammers, A., Dickie, D.A., Hernández, M.V., Wardlaw, J., Rueckert, D., 2018. GAN augmentation: Augmenting training data using generative adversarial networks. arXiv preprint arXiv:1810.10863.

5. Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence;Cruz;Comput. Electron. Agric.,2019

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