Development of low-cost remote sensing tools and methods for supporting smallholder agriculture
Agricultural UAV-based remote sensing tools to facilitate decision-making for increasing productivity in developing countries were developed and tested. Specifically, a high-quality multispectral sensor and sophisticated-yet-user-friendly data processing techniques (software) under an open-access po...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/106637 |
| _version_ | 1855514833579933696 |
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| author | Cucho-Padin, G. Loayza, H. Palacios, S. Balcazar, M. Carbajal, M. Quiróz, R. |
| author_browse | Balcazar, M. Carbajal, M. Cucho-Padin, G. Loayza, H. Palacios, S. Quiróz, R. |
| author_facet | Cucho-Padin, G. Loayza, H. Palacios, S. Balcazar, M. Carbajal, M. Quiróz, R. |
| author_sort | Cucho-Padin, G. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Agricultural UAV-based remote sensing tools to facilitate decision-making for increasing productivity in developing countries were developed and tested. Specifically, a high-quality multispectral sensor and sophisticated-yet-user-friendly data processing techniques (software) under an open-access policy were implemented. The multispectral sensor—IMAGRI-CIP—is a low-cost adaptable multi-sensor array that allows acquiring high-quality and low-SNR images from a UAV platform used to estimate vegetation indexes such as NDVI. Also, a set of software tools that included wavelet-based image alignment, image stitching, and crop classification have been implemented and made available to the remote sensing community. A validation field experiment carried out at the International Potato Center facilities (Lima, Peru) to test the developed tools is reported. A thorough comparison study with a wide-used commercial agricultural camera showed that IMAGRI-CIP provides highly correlated NDVI values (R2≥ 0.8). Additionally, an application field experiment was conducted in Kilosa, Tanzania, to test the tools in smallholder farm settings, featuring high-heterogeneous crop plots. Results showed high accuracy (> 82%) to identify 13 different crops either as mono-crop or as mixed-crops. |
| format | Journal Article |
| id | CGSpace106637 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1066372025-03-13T09:46:14Z Development of low-cost remote sensing tools and methods for supporting smallholder agriculture Cucho-Padin, G. Loayza, H. Palacios, S. Balcazar, M. Carbajal, M. Quiróz, R. normalized difference vegetation index image processing multispectral imagery remote sensing sensors Agricultural UAV-based remote sensing tools to facilitate decision-making for increasing productivity in developing countries were developed and tested. Specifically, a high-quality multispectral sensor and sophisticated-yet-user-friendly data processing techniques (software) under an open-access policy were implemented. The multispectral sensor—IMAGRI-CIP—is a low-cost adaptable multi-sensor array that allows acquiring high-quality and low-SNR images from a UAV platform used to estimate vegetation indexes such as NDVI. Also, a set of software tools that included wavelet-based image alignment, image stitching, and crop classification have been implemented and made available to the remote sensing community. A validation field experiment carried out at the International Potato Center facilities (Lima, Peru) to test the developed tools is reported. A thorough comparison study with a wide-used commercial agricultural camera showed that IMAGRI-CIP provides highly correlated NDVI values (R2≥ 0.8). Additionally, an application field experiment was conducted in Kilosa, Tanzania, to test the tools in smallholder farm settings, featuring high-heterogeneous crop plots. Results showed high accuracy (> 82%) to identify 13 different crops either as mono-crop or as mixed-crops. 2020-01-14 2020-01-20T21:12:14Z 2020-01-20T21:12:14Z Journal Article https://hdl.handle.net/10568/106637 en Open Access Springer Cucho-Padin, G.; Loayza, H.; Palacios, S.; Balcazar, M.; Carbajal, M.; Quiroz, R. 2019. Development of low-cost remote sensing tools and methods for supporting smallholder agriculture. Applied Geomatics. ISSN: 1866-9298. 17 p. |
| spellingShingle | normalized difference vegetation index image processing multispectral imagery remote sensing sensors Cucho-Padin, G. Loayza, H. Palacios, S. Balcazar, M. Carbajal, M. Quiróz, R. Development of low-cost remote sensing tools and methods for supporting smallholder agriculture |
| title | Development of low-cost remote sensing tools and methods for supporting smallholder agriculture |
| title_full | Development of low-cost remote sensing tools and methods for supporting smallholder agriculture |
| title_fullStr | Development of low-cost remote sensing tools and methods for supporting smallholder agriculture |
| title_full_unstemmed | Development of low-cost remote sensing tools and methods for supporting smallholder agriculture |
| title_short | Development of low-cost remote sensing tools and methods for supporting smallholder agriculture |
| title_sort | development of low cost remote sensing tools and methods for supporting smallholder agriculture |
| topic | normalized difference vegetation index image processing multispectral imagery remote sensing sensors |
| url | https://hdl.handle.net/10568/106637 |
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