Oil Palm Yield Data Collection Using Image Processing
This project is an automated drone program integrated with image processing for academic purpose. It will provide students with the methodology, concept and design of an autonomous drone with image processing. This will be illustrated through the training of an ANN for image processing and also prov...
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| Format: | Final Year Project / Dissertation / Thesis |
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2021
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| Online Access: | http://eprints.utar.edu.my/4283/ http://eprints.utar.edu.my/4283/1/17ACB01436_FYP2.pdf |
| _version_ | 1848886116378214400 |
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| author | Yee, Rachel Jee San |
| author_facet | Yee, Rachel Jee San |
| author_sort | Yee, Rachel Jee San |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | This project is an automated drone program integrated with image processing for academic purpose. It will provide students with the methodology, concept and design of an autonomous drone with image processing. This will be illustrated through the training of an ANN for image processing and also provide the basic controls to run an automated drone. The motivation for this project is to solve the traditional way of manually counting oil palm fruits. Spending hours of observation in rough weather conditions is a tedious job and it could be a problem for elderly farmers who are no longer flexible in moving around the big oil palm plantations. In the area of image processing, this job involves different techniques such as pre-processing, feature extraction and ANN. The tools used in training the ANN is the TensorFlow Object Detection API. There are many algorithms in the Object Detection API and three common methods, Faster R-CNN, SSD and YOLO are reviewed for their suitability in object detection. In the end, Faster R-CNN is chosen because its accuracy is the best compared to others, since accuracy is a priority in detecting the production yield for oil palm fruits. This API is important in object classification and counting which serves as the final product in the system. Autonomous drone also plays a big role in this system as it helps in capturing the images from the oil palm plantation. This area involves techniques such as path finding and stabilising in order to control the drone. The completion of this project will take up to two semesters and is divided into two main fields. These two fields include the autonomous drone and image processing area, where each area will be carried out in each semester. In conclusion, an autonomous drone system integrated with image processing can make a huge impact in the field of agriculture, which can change this industry to a more efficient and time-saving industry in terms of calculating the production yield. |
| first_indexed | 2025-11-15T19:33:23Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-4283 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:33:23Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-42832022-01-06T13:08:12Z Oil Palm Yield Data Collection Using Image Processing Yee, Rachel Jee San TA Engineering (General). Civil engineering (General) This project is an automated drone program integrated with image processing for academic purpose. It will provide students with the methodology, concept and design of an autonomous drone with image processing. This will be illustrated through the training of an ANN for image processing and also provide the basic controls to run an automated drone. The motivation for this project is to solve the traditional way of manually counting oil palm fruits. Spending hours of observation in rough weather conditions is a tedious job and it could be a problem for elderly farmers who are no longer flexible in moving around the big oil palm plantations. In the area of image processing, this job involves different techniques such as pre-processing, feature extraction and ANN. The tools used in training the ANN is the TensorFlow Object Detection API. There are many algorithms in the Object Detection API and three common methods, Faster R-CNN, SSD and YOLO are reviewed for their suitability in object detection. In the end, Faster R-CNN is chosen because its accuracy is the best compared to others, since accuracy is a priority in detecting the production yield for oil palm fruits. This API is important in object classification and counting which serves as the final product in the system. Autonomous drone also plays a big role in this system as it helps in capturing the images from the oil palm plantation. This area involves techniques such as path finding and stabilising in order to control the drone. The completion of this project will take up to two semesters and is divided into two main fields. These two fields include the autonomous drone and image processing area, where each area will be carried out in each semester. In conclusion, an autonomous drone system integrated with image processing can make a huge impact in the field of agriculture, which can change this industry to a more efficient and time-saving industry in terms of calculating the production yield. 2021-04-15 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4283/1/17ACB01436_FYP2.pdf Yee, Rachel Jee San (2021) Oil Palm Yield Data Collection Using Image Processing. Final Year Project, UTAR. http://eprints.utar.edu.my/4283/ |
| spellingShingle | TA Engineering (General). Civil engineering (General) Yee, Rachel Jee San Oil Palm Yield Data Collection Using Image Processing |
| title | Oil Palm Yield Data Collection Using Image Processing
|
| title_full | Oil Palm Yield Data Collection Using Image Processing
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| title_fullStr | Oil Palm Yield Data Collection Using Image Processing
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| title_full_unstemmed | Oil Palm Yield Data Collection Using Image Processing
|
| title_short | Oil Palm Yield Data Collection Using Image Processing
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| title_sort | oil palm yield data collection using image processing |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utar.edu.my/4283/ http://eprints.utar.edu.my/4283/1/17ACB01436_FYP2.pdf |