Improving Early Detection of Bud Rot in Oil Palm Through Digital Field Monitoring
Bud Rot (BR) is the most significant phytosanitary threat to oil palm cultivation in Colombia. Early detection is essential for effective curative management, but current methods for detecting BR in adult palms are subjective and unreliable. This research aimed to develop an integrated system for di...
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MDPI AG
2024-10-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/11/2486 |
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| author | Juan Manuel López-Vásquez Diego Alejandro García Cárdenas Carlos Bojacá-Aldana Greicy Andrea Sarria Anuar Morales-Rodríguez |
| author_facet | Juan Manuel López-Vásquez Diego Alejandro García Cárdenas Carlos Bojacá-Aldana Greicy Andrea Sarria Anuar Morales-Rodríguez |
| author_sort | Juan Manuel López-Vásquez |
| collection | DOAJ |
| description | Bud Rot (BR) is the most significant phytosanitary threat to oil palm cultivation in Colombia. Early detection is essential for effective curative management, but current methods for detecting BR in adult palms are subjective and unreliable. This research aimed to develop an integrated system for digital field monitoring and image analysis, testing two detection methods: computer-assisted detection and automatic detection using artificial intelligence (AI). Monthly monitoring was conducted over a 12-month period (January–December 2022) on 672 African oil palms (<i>Elaeis guineensis</i>), 15 years old and susceptible to BR. Disease monitoring focused on the incidence, cumulative incidence, and labor performance based on the number and spatial distribution of palms detected with BR, with or without the use of the device proposed. Results showed that automatic detection using AI had low effectiveness (17.1%), identifying only a small portion of actual cases. In contrast, computer-assisted detection significantly improved accuracy, reaching 78.6% during peak months and reducing detection time by up to two months compared to traditional methods, although, its maximum performance point only reached 4.7 ha/wage. The implementation of digital monitoring provides crucial technological support by considerably improving the effectiveness of early detection in BR curative management. Future advancements in AI-based detection are expected to further improve the efficiency and functionality of this approach. |
| format | Article |
| id | doaj-art-1b9e908cb7d240de8d9e59c63f03e91e |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-1b9e908cb7d240de8d9e59c63f03e91e2025-08-20T01:53:42ZengMDPI AGAgronomy2073-43952024-10-011411248610.3390/agronomy14112486Improving Early Detection of Bud Rot in Oil Palm Through Digital Field MonitoringJuan Manuel López-Vásquez0Diego Alejandro García Cárdenas1Carlos Bojacá-Aldana2Greicy Andrea Sarria3Anuar Morales-Rodríguez4Pest and Disease Program, Colombian Oil Palm Research Center, Cenipalma, Bogotá 111121, ColombiaAgronomy Program, Colombian Oil Palm Research Center, Cenipalma, Bogotá 111121, ColombiaAgronomy Program, Colombian Oil Palm Research Center, Cenipalma, Bogotá 111121, ColombiaPest and Disease Program, Colombian Oil Palm Research Center, Cenipalma, Bogotá 111121, ColombiaPest and Disease Program, Colombian Oil Palm Research Center, Cenipalma, Bogotá 111121, ColombiaBud Rot (BR) is the most significant phytosanitary threat to oil palm cultivation in Colombia. Early detection is essential for effective curative management, but current methods for detecting BR in adult palms are subjective and unreliable. This research aimed to develop an integrated system for digital field monitoring and image analysis, testing two detection methods: computer-assisted detection and automatic detection using artificial intelligence (AI). Monthly monitoring was conducted over a 12-month period (January–December 2022) on 672 African oil palms (<i>Elaeis guineensis</i>), 15 years old and susceptible to BR. Disease monitoring focused on the incidence, cumulative incidence, and labor performance based on the number and spatial distribution of palms detected with BR, with or without the use of the device proposed. Results showed that automatic detection using AI had low effectiveness (17.1%), identifying only a small portion of actual cases. In contrast, computer-assisted detection significantly improved accuracy, reaching 78.6% during peak months and reducing detection time by up to two months compared to traditional methods, although, its maximum performance point only reached 4.7 ha/wage. The implementation of digital monitoring provides crucial technological support by considerably improving the effectiveness of early detection in BR curative management. Future advancements in AI-based detection are expected to further improve the efficiency and functionality of this approach.https://www.mdpi.com/2073-4395/14/11/2486early detectioncurative control<i>Phytophthora palmivora</i>epidemiologyoil palm |
| spellingShingle | Juan Manuel López-Vásquez Diego Alejandro García Cárdenas Carlos Bojacá-Aldana Greicy Andrea Sarria Anuar Morales-Rodríguez Improving Early Detection of Bud Rot in Oil Palm Through Digital Field Monitoring Agronomy early detection curative control <i>Phytophthora palmivora</i> epidemiology oil palm |
| title | Improving Early Detection of Bud Rot in Oil Palm Through Digital Field Monitoring |
| title_full | Improving Early Detection of Bud Rot in Oil Palm Through Digital Field Monitoring |
| title_fullStr | Improving Early Detection of Bud Rot in Oil Palm Through Digital Field Monitoring |
| title_full_unstemmed | Improving Early Detection of Bud Rot in Oil Palm Through Digital Field Monitoring |
| title_short | Improving Early Detection of Bud Rot in Oil Palm Through Digital Field Monitoring |
| title_sort | improving early detection of bud rot in oil palm through digital field monitoring |
| topic | early detection curative control <i>Phytophthora palmivora</i> epidemiology oil palm |
| url | https://www.mdpi.com/2073-4395/14/11/2486 |
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