Towards Automatic Defect Detection in Sugarcane Billets from Images
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-12700-7_57
Reference18 articles.
1. Alencastre-Miranda, M., Davidson, J.R., Johnson, R.M., Waguespack, H., Krebs, H.I.: Robotics for sugarcane cultivation: analysis of billet quality using computer vision. IEEE Rob. Autom. Lett. 3(4), 3828–3835 (2018)
2. Alencastre-Miranda, M., Johnson, R.R., Krebs, H.I.: Convolutional neural networks and transfer learning for quality inspection of different sugarcane varieties. IEEE Trans. Ind. Inf. 17, 787–794 (2020)
3. Chen, S.W., et al.: Counting apples and oranges with deep learning: a data-driven approach. IEEE Rob. Autom. Lett. 2(2), 781–788 (2017)
4. Lecture Notes in Computer Science;S Das Choudhury,2020
5. Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)
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