Innovative Flood Impact Monitoring and Harvest Analysis in Oil Palm Plantations Utilizing Geographic Information Systems and Deep Learning
Floods, as a form of disaster, significantly affect individuals and farmers in impacted areas, particularly through crop damage and the inability to harvest due to prolonged and extensive flooding. Among the most severely affected agricultural sectors are oil palm plantations, which regularly experi...
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| Language: | English |
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MDPI AG
2025-02-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/7/2/44 |
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| author | Supattra Puttinaovarat Supaporn Chai-Arayalert Wanida Saetang Kanit Khaimook Sasikarn Plaiklang Paramate Horkaew |
| author_facet | Supattra Puttinaovarat Supaporn Chai-Arayalert Wanida Saetang Kanit Khaimook Sasikarn Plaiklang Paramate Horkaew |
| author_sort | Supattra Puttinaovarat |
| collection | DOAJ |
| description | Floods, as a form of disaster, significantly affect individuals and farmers in impacted areas, particularly through crop damage and the inability to harvest due to prolonged and extensive flooding. Among the most severely affected agricultural sectors are oil palm plantations, which regularly experience such disruptions annually. Current methods of assistance and relief during flooding rely on field surveys conducted manually by personnel, a process constrained by its time-intensive nature. Moreover, existing applications or platforms do not support the classification and inspection of oil palm plantations affected by floods during harvesting. This research aims to develop a method and application for inspecting oil palm plantations impacted by floods during harvesting. The approach utilizes deep learning and geographic information systems (GIS) to classify and analyze flood-affected areas and determine the ripeness of oil palm bunches on trees, enabling accurate and rapid identification of flood-affected areas. The study results demonstrate that the proposed method achieves a flood classification accuracy ranging from 96.80% to 98.29% and ripeness classification accuracy for oil palm bunches on trees ranging from 97.60% to 99.75%. These findings indicate that the proposed model effectively and efficiently monitors flood-affected areas. Additionally, the developed application serves as a valuable tool for flood management, facilitating timely assistance and relief for farmers impacted by flooding. |
| format | Article |
| id | doaj-art-a20b19aba6fd4cdc954e79c604da06d4 |
| institution | DOAJ |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-a20b19aba6fd4cdc954e79c604da06d42025-08-20T03:11:17ZengMDPI AGAgriEngineering2624-74022025-02-01724410.3390/agriengineering7020044Innovative Flood Impact Monitoring and Harvest Analysis in Oil Palm Plantations Utilizing Geographic Information Systems and Deep LearningSupattra Puttinaovarat0Supaporn Chai-Arayalert1Wanida Saetang2Kanit Khaimook3Sasikarn Plaiklang4Paramate Horkaew5Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, ThailandFaculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, ThailandFaculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, ThailandRamkhamhaeng University, Bangkok 10240, ThailandRajamangala Institute of Technology, Rajamangala University of Technology Isan (RMUTI), Nakhon Ratchasima 30000, ThailandSchool of Computer Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandFloods, as a form of disaster, significantly affect individuals and farmers in impacted areas, particularly through crop damage and the inability to harvest due to prolonged and extensive flooding. Among the most severely affected agricultural sectors are oil palm plantations, which regularly experience such disruptions annually. Current methods of assistance and relief during flooding rely on field surveys conducted manually by personnel, a process constrained by its time-intensive nature. Moreover, existing applications or platforms do not support the classification and inspection of oil palm plantations affected by floods during harvesting. This research aims to develop a method and application for inspecting oil palm plantations impacted by floods during harvesting. The approach utilizes deep learning and geographic information systems (GIS) to classify and analyze flood-affected areas and determine the ripeness of oil palm bunches on trees, enabling accurate and rapid identification of flood-affected areas. The study results demonstrate that the proposed method achieves a flood classification accuracy ranging from 96.80% to 98.29% and ripeness classification accuracy for oil palm bunches on trees ranging from 97.60% to 99.75%. These findings indicate that the proposed model effectively and efficiently monitors flood-affected areas. Additionally, the developed application serves as a valuable tool for flood management, facilitating timely assistance and relief for farmers impacted by flooding.https://www.mdpi.com/2624-7402/7/2/44flood impact monitoringoil palm plantationsdeep learningharvest analysis |
| spellingShingle | Supattra Puttinaovarat Supaporn Chai-Arayalert Wanida Saetang Kanit Khaimook Sasikarn Plaiklang Paramate Horkaew Innovative Flood Impact Monitoring and Harvest Analysis in Oil Palm Plantations Utilizing Geographic Information Systems and Deep Learning AgriEngineering flood impact monitoring oil palm plantations deep learning harvest analysis |
| title | Innovative Flood Impact Monitoring and Harvest Analysis in Oil Palm Plantations Utilizing Geographic Information Systems and Deep Learning |
| title_full | Innovative Flood Impact Monitoring and Harvest Analysis in Oil Palm Plantations Utilizing Geographic Information Systems and Deep Learning |
| title_fullStr | Innovative Flood Impact Monitoring and Harvest Analysis in Oil Palm Plantations Utilizing Geographic Information Systems and Deep Learning |
| title_full_unstemmed | Innovative Flood Impact Monitoring and Harvest Analysis in Oil Palm Plantations Utilizing Geographic Information Systems and Deep Learning |
| title_short | Innovative Flood Impact Monitoring and Harvest Analysis in Oil Palm Plantations Utilizing Geographic Information Systems and Deep Learning |
| title_sort | innovative flood impact monitoring and harvest analysis in oil palm plantations utilizing geographic information systems and deep learning |
| topic | flood impact monitoring oil palm plantations deep learning harvest analysis |
| url | https://www.mdpi.com/2624-7402/7/2/44 |
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