Tomato Maturity Estimation Using Deep Neural Network

In this study, we propose a tomato maturity estimation approach based on a deep neural network. Tomato images were obtained using an RGB camera installed on a monitoring robot and samples were cropped to generate a dataset with which to train the classification model. The classification model is tra...

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Bibliographic Details
Published in:Applied sciences 2023-01, Vol.13 (1), p.412
Main Authors: Kim, Taehyeong, Lee, Dae-Hyun, Kim, Kyoung-Chul, Choi, Taeyong, Yu, Jun Myoung
Format: Article
Language:English
Subjects:
Agricultural production
Agriculture
Classification
convolutional neural networks
Deep learning
Entropy (Information theory)
Expected values
Fruits
Harvest
Horticulture
Maturity
mean–variance loss
Monitoring systems
Neural networks
Probability distribution
robot harvesting
Robotics
Robots
Teaching methods
tomato maturity
Tomatoes
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Summary:In this study, we propose a tomato maturity estimation approach based on a deep neural network. Tomato images were obtained using an RGB camera installed on a monitoring robot and samples were cropped to generate a dataset with which to train the classification model. The classification model is trained using cross-entropy loss and mean–variance loss, which can implicitly provide label distribution knowledge. For continuous maturity estimation in the test stage, the output probability distribution of four maturity classes is calculated as an expected (normalized) value. Our results demonstrate that the F1 score was approximately 0.91 on average, with a range of 0.85–0.97. Furthermore, comparison with the hue value—which is correlated with tomato growth—showed no significant differences between estimated maturity and hue values, except in the pink stage. From the overall results, we found that our approach can not only classify the discrete maturation stages of tomatoes but can also continuously estimate their maturity. Furthermore, it is expected that with higher accuracy data labeling, more precise classification and higher accuracy may be achieved.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13010412