Local Linear Mapping Neural Networks for Pattern Recognition of Plants

Moshou, Dimitrios; Keteiaere, Bart De; Vrindts, Eis; Kennes, Patrik; Baerdemaeker, Josse De; Ramón, Herman

doi:10.1016/s1474-6670(17)36042-1

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…Both sensor systems, the optoelectronic systems and the image analysis system, have been successfully tested under field conditions (Felton & McCloy, 1992; Vrindts & de Baerdemaeker, 1997; Biller, 1998; Moshou et al. , 1998; Sökefeld et al.…”

Section: Introductionmentioning

confidence: 99%

Practical experiences with a system for site‐specific weed control in arable crops using real‐time image analysis and GPS‐controlled patch spraying

2006

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Information on temporal and spatial variation in weed seedling populations within agricultural fields is very important for weed population assessment and management. Primarily, spatial information allows a potential reduction in herbicide use, when post-emergent herbicides are only applied to field sections with high weed infestation levels. This paper presents a system for sitespecific weed control in sugar beet, maize, winter wheat, winter barley, winter rape and spring barley. The system includes on-line weed detection using digital image analysis, computer-based decision making and GlobalPositioning System-controlled patch spraying. In a 2year study, herbicide use with this map-based approach was reduced in winter cereals by 6-81% for herbicides against broad leaved weeds and 20-79% for grass weed herbicides. Highest savings were achieved in cereals followed by sugar beet, maize and winter rape. The efficacy of weed control varied from 85% to 98%, indicating that site-specific weed management will not result in higher infestation levels in the following crops. Keywords: weed distribution, site-specific weed control, patch spraying, on-line weed detection, decision support systems, crop rotation. GERHARDS R & OEBEL H (2006) Practical experiences with a system for site-specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying. Weed Research 46, 185-193.

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Section: Introductionmentioning

confidence: 99%

Practical experiences with a system for site‐specific weed control in arable crops using real‐time image analysis and GPS‐controlled patch spraying

2006

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“…Therefore, optoelectronic sensors and digital cameras were mounted on the tractor to detect weeds in the near range. Vrindts and de Baerdemaeker (1997), Biller (1998) and Moshou et al. (1998) used optoelectronic sensors to measure the reflectance in the green, red and near‐infrared light wave bands.…”

Section: Introductionmentioning

confidence: 99%

Real‐time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley

Gerhards

Christensen²

2003

Weed Research

169

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Information on temporal and spatial variation in weed seedling populations within agricultural fields is very important for weed population assessment and management. Most of all, it allows a potential reduction in herbicide use, when post-emergence herbicides are only applied to field sections with weed infestation levels higher than the economic weed threshold; a review of such work is provided. This paper presents a system for site-specific weed control in sugarbeet (Beta vulgaris L.), maize (Zea mays L.), winter wheat (Triticum aestivum L.) and winter barley (Hordeum vulgare L.), including online weed detection using digital image analysis, computer-based decision making and global positioning systems (GPS)-controlled patch spraying. In a 4-year study, herbicide use with this map-based approach was reduced in winter cereals by 60% for herbicides against broad-leaved weeds and 90% for grass weed herbicides. In sugarbeet and maize, average savings for grass weed herbicides were 78% in maize and 36% in sugarbeet. For herbicides against broad-leaved weeds, 11% were saved in maize and 41% in sugarbeet.

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“…Esta nueva corriente está enfocada en vaios temas como pueden ser el estudio de la convergencia de las redes de neuronas [Citterio et al (1999)], estimación del grado de error cometido por una red de neuronas artificiales [Townsend & Tarassenko (1999)] y estudios sobre la capacidad de aproximación de las redes [Stinchcombe (1999)]. Al mismo tiempo surgen otras corrientes investigadoras apoyándose en los estudios previos con la idea de incluir redes de neuronas fuzzy [Feuring & Lippe (1999)], aplicar los resultados matemáticos a ciertos campos [Moshou et al (1999)] y la traslación de ciertos resultados a otras arquitecturas como las funciones de base radial [Li (1998), Andras (1999), Esposito et al (1999)].…”

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Nuevos métodos para el reconocimiento y clasificación de patrones con redes de neuronas

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Local Linear Mapping Neural Networks for Pattern Recognition of Plants

Cited by 8 publications

References 3 publications

Practical experiences with a system for site‐specific weed control in arable crops using real‐time image analysis and GPS‐controlled patch spraying

Practical experiences with a system for site‐specific weed control in arable crops using real‐time image analysis and GPS‐controlled patch spraying

Real‐time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley

Nuevos métodos para el reconocimiento y clasificación de patrones con redes de neuronas

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