Fruit Ripeness Prediction Based on DNN Feature Induction from Sparse Dataset

1. Prediction of ripeness stages of fruit trees by combining machine learning techniques and deep learning networks

2. P. Waldron, “$4.7 million to sips and bti for more tomato research,” [Online]. Available: https://btiscience.org/explore-bti/news/post/4-7-million-sips-bti-tomato-research/, 2014.

3. Reconfiguration of the achene and receptacle metabolic networks during strawberry fruit development10.1104/pp.108.120691 ↗

4. Learning manipulation actions from a few demonstrations

5. Goodfellow · Deep Learning, · 2016 · 180

6. J. West, D. Ventura and S. Warnick, “Spring research presentation: A theoretical foundation for inductive transfer,” Brigham Young University, College of Physical and Mathematical Sciences, 2007. Archived from the original on 2007-08-01, Retrieved 2007-08-05, [Online]. Available: https://web.archive.org/web/20070801120743/http://cpms.byu.edu/springresearch/abstract-entry?id = 861.

7. Image processing and machine learning for automated fruit grading system: A technical review10.5120/14209-2455 ↗

8. Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modeling of ripening behavior10.1515/intag-2016-0025 ↗

9. Identification of artificially ripened fruits using smart phones

10. Multivariate analysis and machine learning for ripeness classification of cape gooseberry fruits

11. Ripeness classification of bananas using an artificial neural network10.1007/s13369-018-03695-5 ↗

12. Machine vision system in precision agriculture for crop farming10.3390/jimaging5120089 ↗

13. Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images10.1016/j.biosystemseng.2018.09.014 ↗

14. Automatic counting of rice plant numbers after transplanting using low altitude uav images

15. Fruit recognition from images using deep learning10.2478/ausi-2018-0002 ↗

16. Identification of artificially ripened fruits using machine learning

17. S. Sakib, Z. Ashrafi and M. A. B. Siddique, “Implementation of fruits recognition classifier using convolutional neural network algorithm for observation of accuracies for various hidden layers,” arXiv preprint arXiv: 1904.00783v6, 2019.

18. Real-time hyperspectral imaging for the infield estimation of strawberry ripeness with deep learning10.1016/j.aiia.2020.04.003 ↗

19. Classification of ripening of banana fruit using convolution neural networks

20. Fruit classification for retail stores using deep learning

21. A review of convolution neural network applied to fruit image processing10.3390/app10103443 ↗

22. Facial UV map completion for pose-invariant face recognition: A novel adversarial approach based on coupled attention residual UNets10.1186/s13673-020-00250-w ↗

23. A novel hybrid CNN-SVM classifier for recognizing handwritten digits10.1016/j.patcog.2011.09.021 ↗

24. Image classification using biomimetric pattern recognition with convolutional neural networks features

25. Plant disease and pest detection using deep learning-based features10.3906/elk-1809-181 ↗

26. Image recognition using convolutional neural network combined with ensemble learning algorithm

27. A comparison between support vector machine (SVM) and convolutional neural network (CNN) models for hyperspectral image classification

28. CNN-Svm learning approach based human activity recognition

29. HSFE network and fusion model based dynamic hand gesture recognition

30. Multiple human tracking in drone image10.1007/s11042-018-6141-z ↗

31. CNN-based visual/auditory feature fusion method with frame selection for classifying video events,”

32. Support-vector networks10.1007/BF00994018 ↗

33. Y. LeCun, “The MNIST database of handwritten digits,” Courant Institute, NYU Corinna Cortes, Google Labs, New York Christopher J.C. Burges, Microsoft Research, Redmond, 1998.

34. CIFAR-10 (Canadian Institute For Advanced Research), “AI progress measurement,” Electronic Frontier Foundation. 2017-06-12. Retrieved 2017-12-11. 2017.

35. Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis10.1175/1520-0493(1987)115<1825:OALOMA>2.0.CO;2 ↗

36. 5 way to deal with the lack of data in machine learning

37. A survey on image augmentation for deep learning10.1186/s40537-019-0197-0 ↗

38. Research on the deep learning of the small sample data based on transfer learning

39. Using deep neural network with small dataset to predict martial defects10.1016/j.matdes.2018.11.060 ↗

40. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv: 1409.1556, 2014.

41. K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition,” arXiv preprint arXiv: 1512.03385, 2015.10.1109/CVPR.2016.90 ↗

42. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., “Going deeper with convolutions, inception network,” arXiv preprint arXiv: 1409.4842v1, 2014.10.1109/CVPR.2015.7298594 ↗

43. A. G. Howard, M. Zhu, B. Chen and D. Kalenichenko, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv: 1704.04861, 2017.

44. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” arXiv preprint arXiv: 1801.04381v4, 2019.10.1109/CVPR.2018.00474 ↗

45. Support-vector networks10.1007/BF00994018 ↗