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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Monitoring the Growth of Tomatoes in Real Time with Deep Learning-based Image Segmentation

Author 1: Sigit Widiyanto
Author 2: Dheo Prasetyo Nugroho
Author 3: Ady Daryanto
Author 4: Moh Yunus
Author 5: Dini Tri Wardani

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0121247

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 12, 2021.

  • Abstract and Keywords
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Abstract: Increasing agricultural productivity such as tomatoes needs to be increased, considering the consumption growth reaches 6.34% per year. Efforts to increase productivity can be made through several methods, such as counting and predicting the time of fruit to be harvested. This information is a a visual problem, so computer vision should solve it as an automation method in the industry world. With this information, the farmer can monitor the tomato fruit growth. The proposed method is a framework that has been implemented in real-time processing. To obtain growth information of tomatoes, the tomato area can be used as a region of interest (ROI) every week or another scheduled time. As the challenge of this research, this ROI can be extracted using segmentation analysis. The segmentation method used is Mask Region-Convolutional Network (R-CNN) with ResNet101 architecture. The accuracy of this method is obtained from the similarity value between the proposed method and the ground truth used, namely 97.34% using the Dice Coefficient and 94.83% using the Jaccard Coefficient. This result indicates that the method can extract the ROI information with high accuracy. So, the result can be used as a reference for the farmer to treat each tomato plant.

Keywords: Deep learning; Mask R-CNN; segmentation; tomato; growth

Sigit Widiyanto, Dheo Prasetyo Nugroho, Ady Daryanto, Moh Yunus and Dini Tri Wardani, “Monitoring the Growth of Tomatoes in Real Time with Deep Learning-based Image Segmentation” International Journal of Advanced Computer Science and Applications(IJACSA), 12(12), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121247

@article{Widiyanto2021,
title = {Monitoring the Growth of Tomatoes in Real Time with Deep Learning-based Image Segmentation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121247},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121247},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {12},
author = {Sigit Widiyanto and Dheo Prasetyo Nugroho and Ady Daryanto and Moh Yunus and Dini Tri Wardani}
}


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