Drone-based surveillance of palm tress ecosystems
This paper presents a novel surveillance system designed to identify the health status of oil palm trees by leveraging MATLAB object detection and deep learning techniques. The study aims to improve the accuracy and efficiency of palm health detection by integrating MATLAB's initial object reco...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Semarak Ilmu Publishing
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/117056/ http://psasir.upm.edu.my/id/eprint/117056/1/117056.pdf |
| _version_ | 1848867154661736448 |
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| author | Mansor, Ya’akob Baki, Sharudin Omar Sahwee, Zulhilmy Mengyue, Cheng Wu, Yuanyuan |
| author_facet | Mansor, Ya’akob Baki, Sharudin Omar Sahwee, Zulhilmy Mengyue, Cheng Wu, Yuanyuan |
| author_sort | Mansor, Ya’akob |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents a novel surveillance system designed to identify the health status of oil palm trees by leveraging MATLAB object detection and deep learning techniques. The study aims to improve the accuracy and efficiency of palm health detection by integrating MATLAB's initial object recognition with advanced deep learning algorithms. The initial phase of the research focuses on elucidating the challenges associated with detecting palm tree health issues using conventional image processing methods in MATLAB. Results indicate that traditional MATLAB object detection methods encounter difficulties in accurately identifying palm tree crowns and assessing their health status due to various factors such as the complexity of crown morphology, lighting variations, environmental conditions, limited feature discrimination, reliance on handcrafted features, and challenges in adaptation and generalization. Subsequently, the study proposes a second stage to enhance the accuracy and efficiency of palm tree health detection through the implementation of a deep learning approach using Faster R-CNN, addressing the limitations identified in the initial phase. Analysis of experimental results demonstrates a rapid increase in accuracy to nearly 100% early in the training process, indicating efficient learning and classification capabilities of the model. Moreover, a significant decrease in Root Mean Square Error (RMSE) at the outset of training signifies a reduction in prediction errors, followed by stabilization at a low level, suggesting that the model's predictions closely align with actual targets in the training data. Furthermore, the loss graph exhibits a similar trend to the RMSE graph, corroborating the effectiveness of RMSE as a common loss function for regression problems. Overall, this research contributes to the advancement of oil palm tree health detection systems, providing valuable insights for future developments in agricultural surveillance and monitoring technologies. |
| first_indexed | 2025-11-15T14:31:59Z |
| format | Article |
| id | upm-117056 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:31:59Z |
| publishDate | 2024 |
| publisher | Semarak Ilmu Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1170562025-04-23T07:35:25Z http://psasir.upm.edu.my/id/eprint/117056/ Drone-based surveillance of palm tress ecosystems Mansor, Ya’akob Baki, Sharudin Omar Sahwee, Zulhilmy Mengyue, Cheng Wu, Yuanyuan This paper presents a novel surveillance system designed to identify the health status of oil palm trees by leveraging MATLAB object detection and deep learning techniques. The study aims to improve the accuracy and efficiency of palm health detection by integrating MATLAB's initial object recognition with advanced deep learning algorithms. The initial phase of the research focuses on elucidating the challenges associated with detecting palm tree health issues using conventional image processing methods in MATLAB. Results indicate that traditional MATLAB object detection methods encounter difficulties in accurately identifying palm tree crowns and assessing their health status due to various factors such as the complexity of crown morphology, lighting variations, environmental conditions, limited feature discrimination, reliance on handcrafted features, and challenges in adaptation and generalization. Subsequently, the study proposes a second stage to enhance the accuracy and efficiency of palm tree health detection through the implementation of a deep learning approach using Faster R-CNN, addressing the limitations identified in the initial phase. Analysis of experimental results demonstrates a rapid increase in accuracy to nearly 100% early in the training process, indicating efficient learning and classification capabilities of the model. Moreover, a significant decrease in Root Mean Square Error (RMSE) at the outset of training signifies a reduction in prediction errors, followed by stabilization at a low level, suggesting that the model's predictions closely align with actual targets in the training data. Furthermore, the loss graph exhibits a similar trend to the RMSE graph, corroborating the effectiveness of RMSE as a common loss function for regression problems. Overall, this research contributes to the advancement of oil palm tree health detection systems, providing valuable insights for future developments in agricultural surveillance and monitoring technologies. Semarak Ilmu Publishing 2024-11-29 Article PeerReviewed text en cc_by_nc_4 http://psasir.upm.edu.my/id/eprint/117056/1/117056.pdf Mansor, Ya’akob and Baki, Sharudin Omar and Sahwee, Zulhilmy and Mengyue, Cheng and Wu, Yuanyuan (2024) Drone-based surveillance of palm tress ecosystems. Journal of Advanced Research in Applied Sciences and Engineering Technology, 60 (3). ISSN 2462 -1943 https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/12706 10.37934/araset.60.3.7789 |
| spellingShingle | Mansor, Ya’akob Baki, Sharudin Omar Sahwee, Zulhilmy Mengyue, Cheng Wu, Yuanyuan Drone-based surveillance of palm tress ecosystems |
| title | Drone-based surveillance of palm tress ecosystems |
| title_full | Drone-based surveillance of palm tress ecosystems |
| title_fullStr | Drone-based surveillance of palm tress ecosystems |
| title_full_unstemmed | Drone-based surveillance of palm tress ecosystems |
| title_short | Drone-based surveillance of palm tress ecosystems |
| title_sort | drone-based surveillance of palm tress ecosystems |
| url | http://psasir.upm.edu.my/id/eprint/117056/ http://psasir.upm.edu.my/id/eprint/117056/ http://psasir.upm.edu.my/id/eprint/117056/ http://psasir.upm.edu.my/id/eprint/117056/1/117056.pdf |