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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 충남대학교 농업과학연구소 Korean Journal of Agricultural Science Korean Journal of Agricultural Science Vol.49 No.4
- 발행연도
- 2022.12
- 수록면
- 837 - 845 (9page)
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초록· 키워드
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In this study, tomato maturity was estimated based on deep learning for a harvesting robot. Tomato images were obtained using a RGB camera installed on a monitoring robot, which was developed previously, and the samples were cropped to 128 × 128 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the mean-variance loss was used to learn implicitly the distribution of the data features by class. In the test stage, the tomato maturity was estimated as a continuous index, which has a range of 0 to 1, by calculating the expected class value. The results show that the F1-score of the classification was approximately 0.94, and the performance was similar to that of a deep learning-based classification task in the agriculture field. In addition, it was possible to estimate the distribution in each maturity stage. From the results, it was found that our approach can not only classify the discrete maturation stages of the tomatoes but also can estimate the continuous maturity.
목차
Abstract
Introduction
Materials and Methods
Results and Discussion
References
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