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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: Method of budding detection with YOLO (You Only Look Once) for determination of the best time to plucking tealeaves is proposed. In order to get the best quality and quantity of tealeaves, it is very important to determine the best time to plucking date. It is most likely that the number of days elapsed after the budding of the tealeaves are the most effective for determine the best plucking day. Therefore, method for detect the budding is getting much important. In this paper, YOLO-based object detection is proposed. Hyperparameter of the YOLO has to be optimized. Also, a comparative study is conducted for the resolution of the cameras used for acquisition of tealeaves from a point of view for learning performance of YOLO. Through experiments, it is found that the proposed method for detection of budding is effective in terms of learning performance for getting the best quality and quantity of tealeaves harvested.
Kohei Arai and Yoho Kawaguchi, “Method of Budding Detection with YOLO-based Approach for Determination of the Best Time to Plucking Tealeaves” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150564
@article{Arai2024,
title = {Method of Budding Detection with YOLO-based Approach for Determination of the Best Time to Plucking Tealeaves},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150564},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150564},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {Kohei Arai and Yoho Kawaguchi}
}
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.