Evaluation of moisture index and vegetation performance in a coffee crop by remote sensing using Unmanned Aerial Systems
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| Nhiều tác giả: | , , |
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| Định dạng: | artículo original |
| Trạng thái: | Versión publicada |
| Ngày xuất bản: | 2025 |
| Miêu tả: | The coffee production sector constantly faces threats from market conditions and extreme weather changes, which affect its yield. Remote sensing technologies serve as a tool to contribute to efficient crop management. This study evaluated the behavior of moisture and vegetation indices in a coffee plantation under agronomic systems of sun and shade using remote sensors. Direct measurements of moisture, height, and canopy diameter were taken through field sampling and indirectly through LiDAR sensors and multispectral images using unmanned aerial vehicles. Geospatial analysis, at resolutions of 5, 10, and 20 cm/px, determined that the diameter of coffee trees had lower error rates with increasing resolution, contrary to tree height, where a resolution of 10 cm/pixel resulted in less error compared to observed data. Statistical analysis showed significant differences in vegetation indices NDVI and water NDWI between sun and shade treatments concerning water consumption. NDVI exhibited a strong correlation with water consumption in the sun treatment (proportional), while NDWI showed a strong proportional correlation with consumption in the shade treatment and an insignificant correlation in the sun treatment. LiDAR sensors proved to be a useful tool for obtaining height and diameter information in coffee trees, while multispectral images are an option for estimating crop water consumption. |
| Quốc gia: | Portal de Revistas TEC |
| Tổ chức giáo dục: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| Ngôn ngữ: | Español |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/7133 |
| Truy cập trực tuyến: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/7133 |
| Từ khóa: | moisture index vegetation index multiespectral imaging agronomic management remote sensing coffe índice de humedad índice de vegetación imágenes multiespectral manejo agronómico sensores remotos café |