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

Andújar Sánchez, Dionisio and Rueda-Ayala, Víctor and Moreno Párrizas, Hugo and Rosell-Polo, Joan R. and Escolá, Alexandre and Valero Ubierna, Constantino and Gerhards, Roland and Fernández-Quintanilla, César and Dorado, José and 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.

Description

Title: Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor
Author/s:
  • 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.
Item Type: Article
Título de Revista/Publicación: Sensors
Date: 2013
ISSN: 1424-8220
Volume: 13
Subjects:
Freetext Keywords: optical sensors; tree stem detection; state tree classification; LIDAR; light curtain transmission
Faculty: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Department: Ingeniería Rural [hasta 2014]
UPM's Research Group: LPF_TAGRALIA
Creative Commons Licenses: None

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Abstract

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.

More information

Item ID: 32547
DC Identifier: http://oa.upm.es/32547/
OAI Identifier: oai:oa.upm.es:32547
DOI: 10.3390/s131114662
Official URL: http://www.mdpi.com/1424-8220/13/11/14662
Deposited by: Profesor Constantino Valero Ubierna
Deposited on: 29 Oct 2014 14:39
Last Modified: 29 Oct 2014 14:50
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