Pineapple biomass estimation using unmanned aerial vehicle in various forcing stage: Vegetation index approach from ultra-high-resolution image
Pineapple becomes number 4th highest fruit production in Indonesia in 2020 and is one of the mainstay export commodities. Estimates of pineapple production using high-resolution images from drones are extremely rare, whereas production estimates for popular commodities do not pay attention to plant...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/88d84dcf432043b7ae5a594d2935e986 |
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| Sumario: | Pineapple becomes number 4th highest fruit production in Indonesia in 2020 and is one of the mainstay export commodities. Estimates of pineapple production using high-resolution images from drones are extremely rare, whereas production estimates for popular commodities do not pay attention to plant growth stages. This study aims to estimate pineapple biomass using UAVs at various pineapple growth stages to obtain the best formulation. Aerial photographs were taken using the Quest UAV, equipped with visible light and near infra-red camera. Ultra-high-resolution aerial photographs were taken using a UAV with a multispectral camera then transformed into a vegetation index (GDVI, NDVI, OSAVI, and TDVI). Images were taken on land plots with plant forcing ages (F) F-5, F-4, F-3, F-3, F-2, F-1, F0, F+1, and F+2. The results show that each pineapple growth stage has a different index to estimate. GDVI can be used to estimate the age of pineapples at F-5, F-4, F-3, and F+1 stage, while OSAVI can be used for F-2, F-1, and F + 2 TDVI for F0. The validation test result using a paired t-test showed that the biomass data on the field's measurement results are no different from the estimate data, so the best vegetation index can be used to estimate biomass. |
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