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Article

Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images

1
Faculty of Science, Yamagata University, 1-4-12 Kojirakawa, Yamagata 990-8560, Japan
2
Elix Inc., Daini Togo Park Building 3F, 8-34 Yonbancho, Chiyoda-ku, Tokyo 102-0081, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Dimitrios Moshou
Sensors 2021, 21(21), 6999; https://doi.org/10.3390/s21216999
Received: 12 September 2021 / Revised: 16 October 2021 / Accepted: 18 October 2021 / Published: 21 October 2021
(This article belongs to the Collection Machine Learning in Agriculture)
Monitoring fruit growth is useful when estimating final yields in advance and predicting optimum harvest times. However, observing fruit all day at the farm via RGB images is not an easy task because the light conditions are constantly changing. In this paper, we present CROP (Central Roundish Object Painter). The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of U-Net. CROP identifies different types of central roundish fruit in an RGB image in varied light conditions, and creates a corresponding mask. Counting the mask pixels gives the relative two-dimensional size of the fruit, and in this way, time-series images may provide a non-contact means of automatically monitoring fruit growth. Although our measurement unit is different from the traditional one (length), we believe that shape identification potentially provides more information. Interestingly, CROP can have a more general use, working even for some other roundish objects. For this reason, we hope that CROP and our methodology yield big data to promote scientific advancements in horticultural science and other fields. View Full-Text
Keywords: deep learning; U-Net; image segmentation; central object; fruit; pear; growth monitor; RGB images deep learning; U-Net; image segmentation; central object; fruit; pear; growth monitor; RGB images
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MDPI and ACS Style

Fukuda, M.; Okuno, T.; Yuki, S. Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images. Sensors 2021, 21, 6999. https://doi.org/10.3390/s21216999

AMA Style

Fukuda M, Okuno T, Yuki S. Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images. Sensors. 2021; 21(21):6999. https://doi.org/10.3390/s21216999

Chicago/Turabian Style

Fukuda, Motohisa, Takashi Okuno, and Shinya Yuki. 2021. "Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images" Sensors 21, no. 21: 6999. https://doi.org/10.3390/s21216999

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