Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN
Agriculture currently serves as a crucial food source for the global population. However, coconut farming, in particular, demands extensive care and maintenance. This research aims to classify coconut trees across various plantation areas utilizing deep learning techniques, specifically through Fast...
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| Format: | Article |
| Language: | English |
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Ital Publication
2024-12-01
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| Series: | Journal of Human, Earth, and Future |
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| Online Access: | https://hefjournal.org/index.php/HEF/article/view/347 |
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| author | Morakot Worachairungreung Nayot Kulpanich Pornperm Sae-ngow Kunyaphat Thanakunwutthirot Kawinphop Anurak Phonpat Hemwan |
| author_facet | Morakot Worachairungreung Nayot Kulpanich Pornperm Sae-ngow Kunyaphat Thanakunwutthirot Kawinphop Anurak Phonpat Hemwan |
| author_sort | Morakot Worachairungreung |
| collection | DOAJ |
| description | Agriculture currently serves as a crucial food source for the global population. However, coconut farming, in particular, demands extensive care and maintenance. This research aims to classify coconut trees across various plantation areas utilizing deep learning techniques, specifically through Faster R-CNN and Mask R-CNN models, based on unmanned aerial vehicle (UAV) imagery. The data collected by both types of RGB UAVs was used for the classification of coconut trees in experimental plots. For the analysis process, aerial photographs obtained from unmanned aerial vehicles, merged with the principles of aerial photography measurement, were analyzed. The research findings revealed that both Faster R-CNN and Mask R-CNN were capable of effectively classifying image data. Nevertheless, to achieve higher accuracy in results, it is essential that the characteristics of the test plots closely align with each other. This study points towards the adoption of a high-resolution tool, ensuring clearer images that facilitate more accurate classification of coconut trees across extensive areas. Consequently, this could lead to more efficient management and maintenance of coconut plantations. Thus, this approach can substantially enhance the efficiency of managing coconut plantations.
Doi: 10.28991/HEF-2024-05-04-02
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| format | Article |
| id | doaj-art-e4cd1fcb54d24a988b6abe97bdab30c5 |
| institution | Kabale University |
| issn | 2785-2997 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Journal of Human, Earth, and Future |
| spelling | doaj-art-e4cd1fcb54d24a988b6abe97bdab30c52025-01-04T10:54:01ZengItal PublicationJournal of Human, Earth, and Future2785-29972024-12-015456057310.28991/HEF-2024-05-04-02162Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNNMorakot Worachairungreung0Nayot Kulpanich1Pornperm Sae-ngow2Kunyaphat Thanakunwutthirot3Kawinphop Anurak4Phonpat Hemwan5Geography and Geo-Informatics Program, Faculty of Humanities and Social Sciences, Suan Sunandha Rajabhat University, Bangkok, 10300,Geography and Geo-Informatics Program, Faculty of Humanities and Social Sciences, Suan Sunandha Rajabhat University, Bangkok, 10300,Geography and Geo-Informatics Program, Faculty of Humanities and Social Sciences, Suan Sunandha Rajabhat University, Bangkok, 10300,Digital Design and Innovation Program, Faculty of Fine and Applied Arts, Suan Sunandha Rajabhat University, Bangkok, 10300,Geography and Geo-Informatics Program, Faculty of Humanities and Social Sciences, Suan Sunandha Rajabhat University, Bangkok, 10300,Department of Geography, Faculty of Social Sciences, Chiang Mai University, Chiang Mai, 50200,Agriculture currently serves as a crucial food source for the global population. However, coconut farming, in particular, demands extensive care and maintenance. This research aims to classify coconut trees across various plantation areas utilizing deep learning techniques, specifically through Faster R-CNN and Mask R-CNN models, based on unmanned aerial vehicle (UAV) imagery. The data collected by both types of RGB UAVs was used for the classification of coconut trees in experimental plots. For the analysis process, aerial photographs obtained from unmanned aerial vehicles, merged with the principles of aerial photography measurement, were analyzed. The research findings revealed that both Faster R-CNN and Mask R-CNN were capable of effectively classifying image data. Nevertheless, to achieve higher accuracy in results, it is essential that the characteristics of the test plots closely align with each other. This study points towards the adoption of a high-resolution tool, ensuring clearer images that facilitate more accurate classification of coconut trees across extensive areas. Consequently, this could lead to more efficient management and maintenance of coconut plantations. Thus, this approach can substantially enhance the efficiency of managing coconut plantations. Doi: 10.28991/HEF-2024-05-04-02 Full Text: PDFhttps://hefjournal.org/index.php/HEF/article/view/347coconutuavsdeep learningmask r-cnnfaster r-cnn. |
| spellingShingle | Morakot Worachairungreung Nayot Kulpanich Pornperm Sae-ngow Kunyaphat Thanakunwutthirot Kawinphop Anurak Phonpat Hemwan Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN Journal of Human, Earth, and Future coconut uavs deep learning mask r-cnn faster r-cnn. |
| title | Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN |
| title_full | Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN |
| title_fullStr | Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN |
| title_full_unstemmed | Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN |
| title_short | Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN |
| title_sort | classification of coconut trees within plantations from uav images using deep learning with faster r cnn and mask r cnn |
| topic | coconut uavs deep learning mask r-cnn faster r-cnn. |
| url | https://hefjournal.org/index.php/HEF/article/view/347 |
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