Potato virus Y and Potato leafroll virus management under climate change in sub-Saharan Africa

Krüger, Kerstin; Waals, J. E. van der

doi:10.17159/sajs.2020/8579

Cited by 3 publications

(1 citation statement)

References 72 publications

(146 reference statements)

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“…These sampling methods can disclose information about the (first) flight activity, the immigration of aphids into and emigration out of the crops as well as the duration of the flight period not only on the local, but also on regional scale ( Bell et al., 2015 ; Harrington and Hullé, 2017 ). Thus, aphid monitoring by aerial sampling is crucial for monitoring aphid vectors of plant viruses and managing plant virus spread ( Krüger and van der Waals, 2020 ). Here, yellow pan traps and suction traps are the most commonly used autonomous and technically standardized trapping devices to assess aphids in arable crops by farmers, agronomists and scientists.…”

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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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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An overview of potato production in Africa

Muthoni

Shimelis²

2023

Potato Production Worldwide

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Aphid species (Hemiptera: Aphididae) recorded for the first time from the Tunisian network

Boukhris-Bouhachem,

Souissi,

Boukteb’s

2024

Preprint

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In a previous paper, one hundred thirty aphid taxa were recorded in Tunisia including 103 species. In this present work, we give additional records from the Cap Bon region in the North East of Tunisia. Eighty-one new species and 8 taxa were added for the first time. These records are based on daily suction trap and weekly yellow trap catches for 14 years from 2005 to 2018. This addition brings the number of aphids from Tunisia to 219 taxa. The aphid species (86) and subspecies (3) from the Aphididae family were ranged in 7 subfamilies: Aphidinae (64 species and 28 genera), Eriosomatinae (9 species and 6 genera), Chaitophorinae (2 species and 2 genera), Greenideinae (2 species and 1 genus), Lachninae (4 species and 3 genera), Calaphinae (6 species and 5 genera), Saltuaphidinae (2 species and 1 genera). All specimens are conserved in microscopic slides in the Plant Protection Laboratory of INRAT. Furthermore, among this list, other potential aphids as virus vectors were identified in this region.

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Potato virus Y and Potato leafroll virus management under climate change in sub-Saharan Africa

Cited by 3 publications

References 72 publications

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

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

An overview of potato production in Africa

Aphid species (Hemiptera: Aphididae) recorded for the first time from the Tunisian network

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