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Harnessing artificial intelligence for analysing the impacts of nectar and pollen feeding in conservation biological control.
Gurr, Geoff M; Liu, Jian; Pogrebna, Ganna.
Afiliação
  • Gurr GM; Gulbali Institute of Agriculture, Water and Environment, Charles Sturt University, Orange, NSW 2800, Australia; School of Agriculture, Environment and Veterinary Sciences, Charles Sturt University, Leeds Parade, Orange, NSW 2800, Australia. Electronic address: ggurr@csu.edu.au.
  • Liu J; Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW 2795, Australia.
  • Pogrebna G; Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW 2795, Australia.
Curr Opin Insect Sci ; 62: 101176, 2024 04.
Article em En | MEDLINE | ID: mdl-38309323
ABSTRACT
Plant-derived foods, such as nectar and pollen, have garnered substantial research attention due to their potential to support natural enemies of pests. This review is a pioneering exploration of the potential for artificial intelligence approaches to provide insights into the factors that drive the success of conservation biological control (CBC). Nectar and pollen were confirmed as key plant food resources for natural enemies. These have been widely used across differing crop systems and provided by a wide range of CBC interventions, such as field margin flower strips. The combined use of parasitoids and predators is revealed as more successful than either of these guilds alone. CBC success was greater in field crops than in vine and berry crops, whilst interventions using dicotyledonous species that produce nectar in addition to pollen were more successful than using grassy interventions.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Néctar de Plantas Limite: Animals Idioma: En Revista: Curr Opin Insect Sci Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Néctar de Plantas Limite: Animals Idioma: En Revista: Curr Opin Insect Sci Ano de publicação: 2024 Tipo de documento: Article