Integration of image processing algorithm and deep learning approaches to monitor ginger plant
This study aims to integrate image processing and deep learning algorithms to monitor the growth of ginger plants. The proposed system is designed to detect ginger plants and track their growth rate effectively. The deep learning algorithm will undergo training using a dataset containing ginger plan...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
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| Online Access: | http://eprints.utar.edu.my/6958/ http://eprints.utar.edu.my/6958/1/Tan_Cheng_Yong_2004838.pdf |
| _version_ | 1848886812150333440 |
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| author | Tan, Cheng Yong |
| author_facet | Tan, Cheng Yong |
| author_sort | Tan, Cheng Yong |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | This study aims to integrate image processing and deep learning algorithms to monitor the growth of ginger plants. The proposed system is designed to detect ginger plants and track their growth rate effectively. The deep learning algorithm will undergo training using a dataset containing ginger plant images, which will allow it to accurately identify and categorize various stages of growth. The image processing techniques will be used to pre-process and enhance the quality of the images to making it easier for the deep learning model to identify the ginger plants. One YOLOv8 based model was developed for detecting and segmenting ginger plants in various growth states. Following the successful detection and segmentation of the plants, another YOLOv8 based model was further developed to segment individual leaves from detected plant. In order to improve the monitoring process, a depth estimation model was used to calculate the distance from the camera to the plants, enabling measurements of the height and leaf area of the ginger plants. The integration of these two methods will provide a more efficient and reliable way to monitor ginger plant growth, which is important for farmers and researchers in the field of agriculture. |
| first_indexed | 2025-11-15T19:44:26Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6958 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:44:26Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-69582025-02-14T06:43:38Z Integration of image processing algorithm and deep learning approaches to monitor ginger plant Tan, Cheng Yong QA75 Electronic computers. Computer science QA76 Computer software This study aims to integrate image processing and deep learning algorithms to monitor the growth of ginger plants. The proposed system is designed to detect ginger plants and track their growth rate effectively. The deep learning algorithm will undergo training using a dataset containing ginger plant images, which will allow it to accurately identify and categorize various stages of growth. The image processing techniques will be used to pre-process and enhance the quality of the images to making it easier for the deep learning model to identify the ginger plants. One YOLOv8 based model was developed for detecting and segmenting ginger plants in various growth states. Following the successful detection and segmentation of the plants, another YOLOv8 based model was further developed to segment individual leaves from detected plant. In order to improve the monitoring process, a depth estimation model was used to calculate the distance from the camera to the plants, enabling measurements of the height and leaf area of the ginger plants. The integration of these two methods will provide a more efficient and reliable way to monitor ginger plant growth, which is important for farmers and researchers in the field of agriculture. 2024-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6958/1/Tan_Cheng_Yong_2004838.pdf Tan, Cheng Yong (2024) Integration of image processing algorithm and deep learning approaches to monitor ginger plant. Final Year Project, UTAR. http://eprints.utar.edu.my/6958/ |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Tan, Cheng Yong Integration of image processing algorithm and deep learning approaches to monitor ginger plant |
| title | Integration of image processing algorithm and deep learning approaches to monitor ginger plant |
| title_full | Integration of image processing algorithm and deep learning approaches to monitor ginger plant |
| title_fullStr | Integration of image processing algorithm and deep learning approaches to monitor ginger plant |
| title_full_unstemmed | Integration of image processing algorithm and deep learning approaches to monitor ginger plant |
| title_short | Integration of image processing algorithm and deep learning approaches to monitor ginger plant |
| title_sort | integration of image processing algorithm and deep learning approaches to monitor ginger plant |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.utar.edu.my/6958/ http://eprints.utar.edu.my/6958/1/Tan_Cheng_Yong_2004838.pdf |