Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.

Baleba SBS, Omondi VO, Aigbedion-Atalor P, Peter E, Diallo S, Agboka KM.

DOI: 10.1093/jisesa/ieag087
Published: 2026-09-01
Container: J Insect Sci
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1093/jisesa/ieag087,
  title = {Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.},
  author = {Baleba SBS and  Omondi VO and  Aigbedion-Atalor P and  Peter E and  Diallo S and  Agboka KM.},
  year = {2026},
  journal = {J Insect Sci},
  doi = {10.1093/jisesa/ieag087},
  url = {https://doi.org/10.1093/jisesa/ieag087}
}

RIS

TY  - JOUR
TI  - Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.
AU  - Baleba SBS
AU  -  Omondi VO
AU  -  Aigbedion-Atalor P
AU  -  Peter E
AU  -  Diallo S
AU  -  Agboka KM.
PY  - 2026
JO  - J Insect Sci
DO  - 10.1093/jisesa/ieag087
UR  - https://doi.org/10.1093/jisesa/ieag087
ER  -

APA

SBS, B., VO, O., P, A., E, P., S, D., & KM., A. (2026). Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.. J Insect Sci. https://doi.org/10.1093/jisesa/ieag087

Source records

europe-pmc · retrieved 2026-09-26T07:08:38.391Z