Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maize crops
LiDAR (Light Detection And Ranging) is a remote-sensing technique for the measurement of the distance between the sensor and a target. A LiDAR-based detection procedure was tested for characterisation of the weed vegetation present in the inter-row area of a maize field. This procedure was based on...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Universitat de Lleida (UdL) |
| Repositorio: | Repositori Obert UdL |
| OAI Identifier: | oai:repositori.udl.cat:10459.1/56616 |
| Acceso en línea: | https://doi.org/10.1016/j.compag.2012.12.012 http://hdl.handle.net/10459.1/56616 |
| Access Level: | acceso abierto |
| Palabra clave: | LiDAR technology Precision crop protection Site-specific weed management Weed discrimination Males herbes -- Control Blat de moro -- Conreu Radar òptic |
| Sumario: | LiDAR (Light Detection And Ranging) is a remote-sensing technique for the measurement of the distance between the sensor and a target. A LiDAR-based detection procedure was tested for characterisation of the weed vegetation present in the inter-row area of a maize field. This procedure was based on the hypothesis that weed species with different heights can be precisely detected and discriminated using non-contact ranging sensors such as LiDAR. The sensor was placed in the front of an all-terrain vehicle, scanning downwards in a vertical plane, perpendicular to the ground, in order to detect the profile of the vegetation (crop and weeds) above the ground. Measurements were taken on a maize field on 3 m wide (0.45 m2) plots at the time of post-emergence herbicide treatments. Four replications were assessed for each of the four major weed species: Sorghum halepense, Cyperus rotundus, Datura ferox and Xanthium strumarium. The sensor readings were correlated with actual, manually determined, height values (r2 = 0.88). With canonical discriminant analysis the high capabilities of the system to discriminate tall weeds (S. halepense) from shorter ones were quantified. The classification table showed 77.7% of the original grouped cases (i.e., 4800 sampling units) correctly classified for S. halepense. These results indicate that LiDAR sensors are a promising tool for weed detection and discrimination, presenting significant advantages over other types of non-contact ranging sensors such as a higher sampling resolution and its ability to scan at high sampling rates. |
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