Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)

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Edge Detection of Strawberries Ripeness Based on Model Optimization Using Intel OpenVINO Toolkit

Authors
Yosef Adhitya Duta Dewangga1, *, Agus Bejo1, Eka Firmansyah1
1Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No. 2 Kampus UGM, Yogyakarta, Indonesia
*Corresponding author. Email: yosef.adhitya.d@mail.ugm.ac.id
Corresponding Author
Yosef Adhitya Duta Dewangga
Available Online 22 May 2023.
DOI
10.2991/978-94-6463-122-7_5How to use a DOI?
Keywords
Big Data; YOLOX; Intel OpenVINO; Fruit Ripeness Detection
Abstract

The improvement of automation and big data analytics with technology is giving big benefits to the agriculture sector. In the open field crops, farming robot technology helps farmers to spray fertilizer. Soil data analysis can help to determine plant treatment. Harvesting robots for picking fruits with computer vision and robotics can support the productivity and quality of crops. To implement this, appropriate hardware and algorithms are important to define. One of the most premium fruits that can be cultivated by using high technology is strawberries. We need to declare the best trade-off between system design (software & hardware) with implementation especially in agricultural sector. In this experiment, the YOLOX algorithm is running to detect the ripeness of strawberries. The algorithms run in two modes: GPU and CPU only. The best results show that the YOLOX-S algorithm, which runs in GPU mode, is 95.75% in precision and 59 fps in throughput. It will be difficult to accommodate a harvesting robot processor that has a GPU. The algorithm is now run in CPU only mode and it gives only 11.31 fps in throughput. Then the proposed model, which is already optimized by Intel OpenVINO, gives better results in throughput, showing 37.15 fps. So, with the proposed optimized model, we can choose CPU-only hardware for affordable hardware implementation.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)
Series
Advances in Biological Sciences Research
Publication Date
22 May 2023
ISBN
978-94-6463-122-7
ISSN
2468-5747
DOI
10.2991/978-94-6463-122-7_5How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yosef Adhitya Duta Dewangga
AU  - Agus Bejo
AU  - Eka Firmansyah
PY  - 2023
DA  - 2023/05/22
TI  - Edge Detection of Strawberries Ripeness Based on Model Optimization Using Intel OpenVINO Toolkit
BT  - Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)
PB  - Atlantis Press
SP  - 46
EP  - 54
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-122-7_5
DO  - 10.2991/978-94-6463-122-7_5
ID  - Dewangga2023
ER  -