Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor

Andújar Sánchez, Dionisio; Rueda-Ayala, Víctor; Moreno Párrizas, Hugo; Rosell-Polo, Joan R.; Escolá, Alexandre; Valero Ubierna, Constantino; Gerhards, Roland; Fernández-Quintanilla, César; Dorado, José y Griepentrog, Hans W. (2013). Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor. "Sensors", v. 13 ; pp. 14662-14675. ISSN 1424-8220. https://doi.org/10.3390/s131114662.

Descripción

Título: Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor
Autor/es:
  • Andújar Sánchez, Dionisio
  • Rueda-Ayala, Víctor
  • Moreno Párrizas, Hugo
  • Rosell-Polo, Joan R.
  • Escolá, Alexandre
  • Valero Ubierna, Constantino
  • Gerhards, Roland
  • Fernández-Quintanilla, César
  • Dorado, José
  • Griepentrog, Hans W.
Tipo de Documento: Artículo
Título de Revista/Publicación: Sensors
Fecha: 2013
Volumen: 13
Materias:
Palabras Clave Informales: optical sensors; tree stem detection; state tree classification; LIDAR; light curtain transmission
Escuela: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Departamento: Ingeniería Rural [hasta 2014]
Grupo Investigación UPM: LPF_TAGRALIA
Licencias Creative Commons: Ninguna

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Resumen

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R 2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.

Más información

ID de Registro: 32547
Identificador DC: http://oa.upm.es/32547/
Identificador OAI: oai:oa.upm.es:32547
Identificador DOI: 10.3390/s131114662
URL Oficial: http://www.mdpi.com/1424-8220/13/11/14662
Depositado por: Profesor Constantino Valero Ubierna
Depositado el: 29 Oct 2014 14:39
Ultima Modificación: 29 Oct 2014 14:50
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