Durian plant health and growth monitoring using image processing
Ahmad, Zahari Awang; Chow, Tan Shie; Asmawi, Mohamad Akmal Muhammad; Abdullah, Abu Hassan; Soh, Ping Jack
Institute of Advanced Engineering and Science
Awang Ahmad, Z., Sie Chow, T., Muhammad Asmawi, M. A., Abdullah, A. H., & Jack Soh, P. (2025). Durian plant health and growth monitoring using image processing. Bulletin of Electrical Engineering and Informatics, 14(3), 1925–1937. https://doi.org/10.11591/eei.v14i3.8550
https://creativecommons.org/licenses/by-sa/4.0/
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
https://creativecommons.org/licenses/by-sa/4.0/
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
https://creativecommons.org/licenses/by-sa/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202505263911
https://urn.fi/URN:NBN:fi:oulu-202505263911
Tiivistelmä
Abstract
The demand for durians has increased considerably, gaining significant popularity in the market. Under the Industrial Revolution 4.0, precision agriculture is expanding globally, utilizing a range of digital technologies to provide the farming industry with crucial information for enhancing farm productivity. For durians to produce high-quality fruit, it is essential that the plants receive sufficient nutrients. Therefore, it is crucial for farmers to monitor the growth rate of durian plants to ensure they receive suitable nutrients for optimum growth. Manual growth monitoring often yields inaccurate results and is prone to human error. Thus, automatic systems for plant image analysis could prove highly beneficial for practical and productive agriculture. This research utilizes the you only look once version 5 (YOLOv5) model alongside an image referencing method for growth monitoring. It begins with the detection of the durian tree, segmenting the leaf area and computing tree size through image referencing. This method achieves a precision of 96% in detecting durian trees from images. Through these images, the growth rate of the durian is assessed through comparisons of canopy growth, stem size, and tree height.
The demand for durians has increased considerably, gaining significant popularity in the market. Under the Industrial Revolution 4.0, precision agriculture is expanding globally, utilizing a range of digital technologies to provide the farming industry with crucial information for enhancing farm productivity. For durians to produce high-quality fruit, it is essential that the plants receive sufficient nutrients. Therefore, it is crucial for farmers to monitor the growth rate of durian plants to ensure they receive suitable nutrients for optimum growth. Manual growth monitoring often yields inaccurate results and is prone to human error. Thus, automatic systems for plant image analysis could prove highly beneficial for practical and productive agriculture. This research utilizes the you only look once version 5 (YOLOv5) model alongside an image referencing method for growth monitoring. It begins with the detection of the durian tree, segmenting the leaf area and computing tree size through image referencing. This method achieves a precision of 96% in detecting durian trees from images. Through these images, the growth rate of the durian is assessed through comparisons of canopy growth, stem size, and tree height.
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