Coffee Leaf Disease Detection Using Transfer Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-28183-9_16
Reference34 articles.
1. Sorte, L.X.B., Ferraz, C.T., Fambrini, F., dos Reis Goulart, R., Saito, J.H.: Coffee leaf disease recognition based on deep learning and texture attributes. Procedia Comput. Sci. 159, 135–144 (2019). Knowledge-Based and Intelligent Information Engineering Systems: Proceedings of the 23rd International Conference KES2019
2. Brahimi, M., Mahmoudi, S., Boukhalfa, K., Moussaoui, A.: Deep interpretable architecture for plant diseases classification. In: 2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 111–116. IEEE (2019)
3. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1807 (2017)
4. DeChant, C., et al.: Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107(11), 1426–1432 (2017)
5. Fan, X., Luo, P., Mu, Y., Zhou, R., Tjahjadi, T., Ren, Y.: Leaf image based plant disease identification using transfer learning and feature fusion. Comput. Electron. Agric. 196, 106892 (2022)
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