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Dynamic segmentation to estimate vine vigor from ground images

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Dynamic segmentation to estimate vine vigor from ground images

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dc.contributor.author Sáiz Rubio, Verónica es_ES
dc.contributor.author Rovira Más, Francisco es_ES
dc.date.accessioned 2016-04-15T15:08:25Z
dc.date.available 2016-04-15T15:08:25Z
dc.date.issued 2012-09
dc.identifier.issn 1695-971X
dc.identifier.uri http://hdl.handle.net/10251/62646
dc.description.abstract [EN] The geographic information required to implement precision viticulture applications in real fields has led to the extensive use of remote sensing and airborne imagery. While advantageous because they cover large areas and provide diverse radiometric data, they are unreachable to most of medium-size Spanish growers who cannot afford such image sourcing. This research develops a new methodology to generate globally-referenced vigor maps in vineyards from ground images taken with a camera mounted on a conventional tractor. This monocular camera was able to sense in the visible, NIR, and UV spectra, selectively isolated with bandpass filters. The versatility of the system was further enhanced by implementing two sampling levels: intensive coverage of 1 m2 and super-intensive for 0.1 m2 . The core of the procedure resides in the algorithm for automatically segmenting the filtered images in such a way that relative differences in canopy vigor were objectively quantified. The calculation of the dynamic threshold involved the mathematical concepts of gradient and curvature. Field results showed that relative differences in vine vigor can be detected from NIR-filtered images and intensive sampling. Furthermore, individual images were successfully merged into a global vigor map that can be directly employed by end-users. Super-intensive sampling and UV perception were not appropriate for building vigor maps, but could be of interest for other agronomical purposes as the early detection of diseases. Field tests proved the feasibility of building global vigor maps from ground-based imagery, and showed the potential of this technique as a predictive instrument for modest-size producers. es_ES
dc.description.abstract [ES] La información requerida para implementar aplicaciones de viticultura de precisión en parcelas reales ha desembocado en el uso extensivo de la teledetección y la detección aérea. Si bien estos métodos son ventajosos por cubrir vastas áreas y proveer diversa información radiométrica, suelen ser inalcanzables para el productor español medio debido a los gastos ocasionados. Esta investigación desarrolla una nueva metodología para generar mapas de vigor con referencias globales basados en imágenes digitales tomadas con una cámara montada sobre un tractor convencional y con capacidad para percibir en el espectro visible, infrarrojo cercano (NIR) y ultravioleta (UV). Para hacer el sistema más versátil se analizaron dos niveles de muestreo: intensivo (1 m2 de cobertura vegetal) y super-intensivo (0,1 m2 de cobertura). El núcleo de la metodología propuesta se basa en el algoritmo de segmentación de imágenes para cuantificar automáticamente diferencias en vigor vegetativo. El cálculo del umbral dinámico se fundamenta en los conceptos matemáticos de gradiente y curvatura. Los resultados obtenidos mostraron que es posible cuantificar diferencias en vigor vegetativo de viñas utilizando el rango espectral del NIR con un muestreo intensivo. El muestreo super-intensivo y la banda espectral UV no resultaron adecuados para esta aplicación, aunque pueden aportar información clave en otras aplicaciones agronómicas. Las pruebas de campo demostraron la viabilidad de generar mapas georreferenciados de vigor desde vehículos convencionales, y mostraron el potencial de esta técnica como instrumento predictivo para explotaciones de tamaño medio. es_ES
dc.description.sponsorship The authors would like to express their gratitude to Luis Gil-Orozco Esteve, manager of the winery Bodegas Finca Ardal, as well as the Ministerio de Ciencia e Innovacion for funding this research with Project AGL2009-11731. Appreciation is also extended to Juan Jose Pena Suarez and Montano Perez Teruel for their assistance in the preparation of the vehicle and during the field experiments. en_EN
dc.language Inglés es_ES
dc.publisher Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) es_ES
dc.relation.ispartof Spanish Journal of Agricultural Research es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Dynamic threshold es_ES
dc.subject GPS es_ES
dc.subject NIR es_ES
dc.subject Precision viticulture es_ES
dc.subject UV es_ES
dc.subject Vigor map es_ES
dc.subject Mapas de vigor es_ES
dc.subject Umbralizado dinámico es_ES
dc.subject Viticultura de precisión es_ES
dc.subject.classification INGENIERIA AGROFORESTAL es_ES
dc.title Dynamic segmentation to estimate vine vigor from ground images es_ES
dc.title.alternative Segmentación automática de imágenes digitales para estimar el vigor en viñas es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.5424/sjar/2012103-508-11
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//AGL2009-11731/ES/Percepcion Tridimensional Para Robotica Agricola/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Rural y Agroalimentaria - Departament d'Enginyeria Rural i Agroalimentària es_ES
dc.description.bibliographicCitation Sáiz Rubio, V.; Rovira Más, F. (2012). Dynamic segmentation to estimate vine vigor from ground images. Spanish Journal of Agricultural Research. 10(3):596-604. https://doi.org/10.5424/sjar/2012103-508-11 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.5424/sjar/2012103-508-11 es_ES
dc.description.upvformatpinicio 596 es_ES
dc.description.upvformatpfin 604 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 3 es_ES
dc.relation.senia 258145 es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
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