Detection of Fusarium euwallaceae and its vector Euwallacea fornicatus on pear (Pyrus communis) and in deciduous fruit orchards in South Africa

Engelbrecht, Karyn; Raubenheimer, Inge; Paap, Trudy; Neethling, Emma; Roets, Francois

doi:10.1007/s13314-023-00524-z

Australasian Plant Dis. Notes

2024

DOI: 10.1007/s13314-023-00524-z

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Detection of Fusarium euwallaceae and its vector Euwallacea fornicatus on pear (Pyrus communis) and in deciduous fruit orchards in South Africa

Karyn Engelbrecht,

Inge Raubenheimer,

Trudy Paap

et al.

Abstract: We present the first report of Fusarium euwallaceae and its ambrosia beetle vector Euwallacea fornicatus in deciduous fruit tree orchards in South Africa. Fusarium euwallaceae, is shown to be pathogenic to pear (Pyrus communis) and the beetle can establish viable colonies in this host.

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Cited by 2 publications

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Machine learning for pest detection and infestation prediction: A comprehensive review

Mittal,

Gupta,

Aamash

et al. 2024

WIREs Data Min & Knowl

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Pests pose a major danger to a variety of industries, including agriculture, public health, and ecosystems. Fast and precise pest detection, as well as the ability to predict infestations, are required for effective pest management tactics. This paper provides a comprehensive literature review on this subject to provide an overview of the state of research on pest detection and infestation prediction. The paper investigates and presents background information on the necessity of pest control as well as the difficulty in recognizing pests and forecasting. Several strategies, including approaches to data collection, modeling, and assessment of models, are reviewed in the research described. The authors examine various pest detection methods involving the utilization of convolutional neural networks and several object detection architectures categorized broadly into one‐stage and two‐stage object detection algorithms. Methods for predicting pest infestations that involve regression, classification, and time series forecasting are also thoroughly investigated. The challenges of recognizing pests and predicting infestations are underlined, as are issues with data quality, feature selection, and model interpretability. The report also indicates the limitations to pest detection and infestation prediction as well as intriguing topics for further research on the same. The findings of the literature research demonstrate how Artificial Intelligence, Computer Vision, and the Internet of Things have been applied for Pest Detection and Infestation Prediction. The research serves as a base for surveying and summarizing the approaches utilized for the task of pest detection (an object detection problem) and pest infestation prediction (a forecasting problem) and its findings and recommendations serve as a platform for future study and the development of effective pest management solutions.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Prediction

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Machine learning for pest detection and infestation prediction: A comprehensive review

Mittal,

Gupta,

Aamash

et al. 2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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Fusarium euwallaceae, symbiont of the paninvasive polyphagous shot hole borer, is pathogenic to cultivated but not wild olive trees in South Africa

Crous,

Roets

2024

J Plant Pathol

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Fusarium euwallaceae, vectored by the paninvasive polyphagous shot hole borer beetle (Euwallacea fornicatus), is an emerging threat to trees globally. Proven pathogenic to cultivated deciduous fruits in South Africa, it recently has been isolated from cultivated European (Olea europaea subsp. europaea) and native African (Olea europaea subsp. cuspidata) olive. This potentially threatens both commercial production and native species conservation. However, pathogenicity to these trees is unknown. Three isolates were used in pathogenicity trials of F. euwallaceae towards cultivated European and African olives. Fusarium euwallaceae caused significantly longer lesions than the controls in vascular tissues of inoculated European olive trees, whereas no difference was observed for African olive. We therefore report for the first time that F. euwallaceae is pathogenic to cultivated European olive but not to African olive. As this fungus occludes affected xylem tissues, and thus water flow, olive fruit and oil production might be hampered during droughts, which are predicted to increase in severity and frequency in the main region olives are planted in in South Africa.

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Detection of Fusarium euwallaceae and its vector Euwallacea fornicatus on pear (Pyrus communis) and in deciduous fruit orchards in South Africa

Abstract: We present the first report of Fusarium euwallaceae and its ambrosia beetle vector Euwallacea fornicatus in deciduous fruit tree orchards in South Africa. Fusarium euwallaceae, is shown to be pathogenic to pear (Pyrus communis) and the beetle can establish viable colonies in this host.

Cited by 2 publications

References 13 publications

Machine learning for pest detection and infestation prediction: A comprehensive review

Machine learning for pest detection and infestation prediction: A comprehensive review

Fusarium euwallaceae, symbiont of the paninvasive polyphagous shot hole borer, is pathogenic to cultivated but not wild olive trees in South Africa

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