Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)

<Previous Article In Volume
Next Article In Volume>

Vision-Based Object Detection for Efficient Monitoring in Smart Hydroponic Systems

Authors
Septafiansyah Dwi Putra1, *, Agus Ambarwari1, Imam Asrowardi1, Moh. Harris Imron S. Jaya1
1Internet Engineering Technology, Politeknik Negeri Lampung, Bandar, Lampung, Indonesia
*Corresponding author. Email: septa@polinela.ac.id
Corresponding Author
Septafiansyah Dwi Putra
Available Online 17 February 2024.
DOI
10.2991/978-94-6463-364-1_40How to use a DOI?
Keywords
YOLO; smart hydroponics; AI; object detection
Abstract

With the advancements in technology, smart hydroponic systems have gained popularity as an efficient and sustainable method of cultivation. These systems allow for precise monitoring and control of various parameters such as nutrient levels, pH, temperature, and humidity. To further improve the monitorin capabilities of smart hydroponic systems, integrating object detection using vision-based techniques is proposed. This integration aims to enhance the monitoring process by enabling the system to identify and track specific objects or elements of interest. In this paper, we propose a modified, yet lightweight, object detection model based on the YOLO-v8 architecture.

The proposed model can detect ‘ready’, ‘empty pod’, ‘germination’, ‘pod’, and ‘young’ on the hydroponics palate. The experimental results also demonstrate that precision is improved by a large margin. In fact, as shown in the experiments, the results show a 0.91 score for F1-Confidence curve. Recall rate at different probability thresholds with all classes 91% confidence with F1 over 0,8 except “ready” class.

Copyright
© 2024 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.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
Series
Advances in Engineering Research
Publication Date
17 February 2024
ISBN
978-94-6463-364-1
ISSN
2352-5401
DOI
10.2991/978-94-6463-364-1_40How to use a DOI?
Copyright
© 2024 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  - Septafiansyah Dwi Putra
AU  - Agus Ambarwari
AU  - Imam Asrowardi
AU  - Moh. Harris Imron S. Jaya
PY  - 2024
DA  - 2024/02/17
TI  - Vision-Based Object Detection for Efficient Monitoring in Smart Hydroponic Systems
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
PB  - Atlantis Press
SP  - 421
EP  - 434
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-364-1_40
DO  - 10.2991/978-94-6463-364-1_40
ID  - Putra2024
ER  -