Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN

Morakot Worachairungreung; Nayot Kulpanich; Pornperm Sae-ngow; Kunyaphat Thanakunwutthirot; Kawinphop Anurak; Phonpat Hemwan

doi:10.28991/HEF-2024-05-04-02

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Morakot Worachairungreung Geography and Geo-Informatics Program, Faculty of Humanities and Social Sciences, Suan Sunandha Rajabhat University, Bangkok, 10300,, Thailand
Nayot Kulpanich
nayot.ku@ssru.ac.th
Geography and Geo-Informatics Program, Faculty of Humanities and Social Sciences, Suan Sunandha Rajabhat University, Bangkok, 10300,, Thailand https://orcid.org/0000-0003-3222-989X
Pornperm Sae-ngow Geography and Geo-Informatics Program, Faculty of Humanities and Social Sciences, Suan Sunandha Rajabhat University, Bangkok, 10300,, Thailand
Kunyaphat Thanakunwutthirot Digital Design and Innovation Program, Faculty of Fine and Applied Arts, Suan Sunandha Rajabhat University, Bangkok, 10300,, Thailand
Kawinphop Anurak Geography and Geo-Informatics Program, Faculty of Humanities and Social Sciences, Suan Sunandha Rajabhat University, Bangkok, 10300,, Thailand
Phonpat Hemwan Department of Geography, Faculty of Social Sciences, Chiang Mai University, Chiang Mai, 50200,, Thailand

Vol. 5 No. 4 (2024): December

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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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Worachairungreung, M., Kulpanich, N., Sae-ngow, P., Thanakunwutthirot, K., Anurak, K., & Hemwan, P. (2024). 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, 5(4), 560–573. https://doi.org/10.28991/HEF-2024-05-04-02

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Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN

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Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN

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Assessment of the Presence of Pharmaceutical Compounds in Wastewaters and in Aquatic Environment

Alternative Fuel: Hydrogen and its Thermodynamic Behaviour

Utilization Management to Ensure Clean Water Sources in Coastal Areas

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