Satellite information fusion with low-altitude unmanned aerial vehicle flights for estimating weed coverage
The detection, geolocation, and classification of weeds in agricultural fields is a problem of interest associated with Precision Agriculture (PA). The main contribution of this work is to describe a workflow (feasible to automate) based on open-source software tools and open information to: 1) meas...
Guardado en:
| Autores principales: | , , , , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/157807 |
| Aporte de: |
| Sumario: | The detection, geolocation, and classification of weeds in agricultural fields is a problem of interest associated with Precision Agriculture (PA). The main contribution of this work is to describe a workflow (feasible to automate) based on open-source software tools and open information to: 1) measure the spatiotemporal evolution of weed patches through satellite images, and 2) register high-resolution images (taken at low altitude) on top of the satellite image to identify the weeds that compose the detected patches. To merge the satellite and low-altitude information, the following problems must be solved: 1) correct distortions in the acquired images; 2) develop an image formation model that allows registering the low-altitude image on top of the satellite image, and 3) analyze green indices to measure patch coverage in both multiespectral satellite images and RGB images obtained from a camera mounted on an unmanned aerial vehicle. Finally, the feasibility of merging information is demonstrated through an analysis of the correlation in the coverage measures obtained from satellite and low-altitude images. |
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