Incorporating Dilation Convolution into Mask Region Convolution Neural Network for Advanced Fruit Classification and Freshness Evaluation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62814-6_4
Reference27 articles.
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2. Faisal, M., Albogamy, F., Elgibreen, H., Algabri, M., Alqershi, F.A.: Deep learning and computer vision for estimating date fruits type, maturity level, and weight. IEEE Access 8, 206770–206782 (2020). https://doi.org/10.1109/ACCESS.2020.3037948
3. Palakodati, S.S.S., Chirra, V.R.R., Yakobu, D., Bulla, S.: Fresh and Rotten Fruits Classification Using CNN and Transfer Learning. Rev. d’Intelligence Artif. 34(5), 617–622 (2020). https://doi.org/10.18280/ria.340512
4. Ireri, D., Belal, E., Okinda, C., Makange, N., Ji, C.: A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artificial Intelligence in Agriculture 2, 28–37 (2019). https://doi.org/10.1016/j.aiia.2019.06.001
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