Bestimmungsschlüssel für geflügelte Blattläuse in Gelbschalen

Müller, sc. phil. Fritz Paul

doi:10.1080/03235407509431157

Cited by 10 publications

(2 citation statements)

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“…The traps were emptied every day, and the aphid species were determined. Keys by Müller (1966) and Taylor et al (1980) were used. Depending on the year of the research, the aphid plot was located at most 30 m away from the plots with tested cultivars.…”

Section: Methodsmentioning

confidence: 99%

The Rate of Virus Spread in New Potato Cultivars in the North of Poland

Wrobel

2015

Potato Res.

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The aim of this research was to assess the rate of increase in the level of tuber infection by PVY, PVM, and PLRV during three consecutive years of multiplication in the field for 17 new cultivars registered in Poland in 2009-2011 and for two cultivars not listed in the registry but popularly cultivated. The research was conducted in Bonin near Koszalin (north-western Poland) in 2010-2013. Tuber infection was assessed using DAS ELISA in a grow-out test in the winter-spring period. Among the 19 cultivars examined, eight had high resistance to PVY (above grade 8 on a scale of 1-9); during the 3-year research period, they were not infected. Also, cv. Gawin seemed more resistant than previously assumed. In contrast, cvs. Hermes and Sylvana, which were rated in the Netherlands as quite resistant, were clearly very susceptible (grade 3-4) in Polish conditions, as within 2 years 100% of tubers were infected with this virus. The greatest susceptibility to PVM was shown by cvs. Danuta and Stasia, 50% of which were infected, despite moderate exposure to the virus. Cultivars Zenia, Etiuda, Jubilat, and Viviana appeared very resistant to PVM as the number of infected tubers did not exceed 5%. PVM was not detected in the tubers of cvs. Bursztyn, Gustaw, and Legenda, which confirmed that these cultivars possess the Rm gene. Not all cultivars regarded susceptible (grade 3-4) to PLRV were infected. The virus was not found in tubers of cv. Bursztyn, while in cv. Hermes, assessed in the Netherlands as being quite resistant, almost 40% of tubers were infected.

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Section: Methodsmentioning

confidence: 99%

The Rate of Virus Spread in New Potato Cultivars in the North of Poland

Wrobel

2015

Potato Res.

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“…on dark soil ( Döring and Chittka, 2007 ; Döring, 2014 ). Although selective for certain aphid species ( Karl, 1991 ), over 90 aphid species have been described to be frequently caught in yellow pan traps in Central Europe, among them around 35 species with agricultural relevance ( Müller, 1975 ; Dubnik, 1991 ; Basky, 1993 ). Due to their good trapping ability, pan traps are used in a wide range of arable crops, including e.g.…”

Section: Aphid Monitoringmentioning

confidence: 99%

From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring

Batz¹,

Will

Thiel³

et al. 2023

Front. Plant Sci.

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Insect monitoring has gained global public attention in recent years in the context of insect decline and biodiversity loss. Monitoring methods that can collect samples over a long period of time and independently of human influences are of particular importance. While these passive collection methods, e.g. suction traps, provide standardized and comparable data sets, the time required to analyze the large number of samples and trapped specimens is high. Another challenge is the necessary high level of taxonomic expertise required for accurate specimen processing. These factors create a bottleneck in specimen processing. In this context, machine learning, image recognition and artificial intelligence have emerged as promising tools to address the shortcomings of manual identification and quantification in the analysis of such trap catches. Aphids are important agricultural pests that pose a significant risk to several important crops and cause high economic losses through feeding damage and transmission of plant viruses. It has been shown that long-term monitoring of migrating aphids using suction traps can be used to make, adjust and improve predictions of their abundance so that the risk of plant viruses spreading through aphids can be more accurately predicted. With the increasing demand for alternatives to conventional pesticide use in crop protection, the need for predictive models is growing, e.g. as a basis for resistance development and as a measure for resistance management. In this context, advancing climate change has a strong influence on the total abundance of migrating aphids as well as on the peak occurrences of aphids within a year. Using aphids as a model organism, we demonstrate the possibilities of systematic monitoring of insect pests and the potential of future technical developments in the subsequent automated identification of individuals through to the use of case data for intelligent forecasting models. Using aphids as an example, we show the potential for systematic monitoring of insect pests through technical developments in the automated identification of individuals from static images (i.e. advances in image recognition software). We discuss the potential applications with regard to the automatic processing of insect case data and the development of intelligent prediction models.

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Eine neue Aphis-Art von Astragalus cicer

Müller

1986

Dtsch. Entomol. Z.

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Bestimmungsschlüssel für geflügelte Blattläuse in Gelbschalen

Cited by 10 publications

References 0 publications

The Rate of Virus Spread in New Potato Cultivars in the North of Poland

The Rate of Virus Spread in New Potato Cultivars in the North of Poland

From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring

Eine neue Aphis-Art von Astragalus cicer

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