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Machine Learning-Driven Predictive Modelling for Laboratory Evaluation of the Toxicological Efficacy of Cymbopogon citratus Ethanolic Extract Against Acanthoscelides obtectus
Subject area: Science,Engineering and Technology · Area of research: Machine Learning and Pest Management
Abstract
Post-harvest storage losses inflicted by the bean weevil (Acanthoscelides obtectus) threaten agricultural economic stability and legume food security across Nigeria. While conventional pest management depends extensively on synthetic chemical treatments, issues surrounding environmental resilience, applicator health, and toxic residue deposition require eco-friendly botanical alternatives. This study evaluates the laboratory contact toxicity of an ethanolic leaf extract of Cymbopogon citratus (Lemongrass) against adult A. obtectus and introduces a machine learning framework to model time-dose-mortality dynamics. This is an advancement in smart farming that outperforms conventional linear calculations, especially when predicting pest control outcomes. Fresh C. citratus leaves were collected, air-dried, and subjected to cold maceration extraction using absolute ethanol. Gas Chromatography-Mass Spectrometry (GC-MS) analysis of the crude extract revealed a rich matrix of bioactive constituents, prominently dominated by citral isomers (geranial and neral), myrcene, and geraniol. In controlled laboratory bioassays, adult bean weevils were exposed via filter-paper impregnation to graded volumetric doses of the ethanolic extract (0.0 to 10.0 ml). Empirical observations demonstrated strong, rapid contact toxicity, with a 10 ml dose inducing a 50% population mortality rate within 10 minutes, accelerating to complete population knockdown (100%) within 30 minutes, while untreated control cohorts experienced zero mortality. To expand these empirical thresholds into a predictive tool, an analytical dataset (n = 1,200) mapping extract dose, exposure intervals, temperature (25–30 °C), and relative humidity (60–75%) was evaluated using a Random Forest Regressor and Support Vector Regression (SVR). The Random Forest architecture yielded superior predictive performance (R2 = 0.988, MAE = 1.32%), isolating an optimal operational application benchmark of 6.8 ml to achieve near-total eradication (98.5%) within 45 minutes while minimizing active extract waste. This integration of botanical toxicology and ensemble learning provides a standardized, scalable framework for modern smart-storage pest management.
Keywords
Acanthoscelides obtectus, contact toxicity, Cymbopogon citratus, predictive toxicology, Random Forest Regressor
References
[1] Adebayo RA, Omoloye AA. Insecticidal activity of aromatic plant extracts against stored-product beetles. Journal of Stored Products Research. 2019;82:45–52.
[2] Olotuah OF. Effect of spray treatments on the production of an indigenous cowpea, Erusu. Scholarly Journal of Agricultural Science. 2013;3:62–65.
[3] Adedire CO, Akinkurolere RO. Efficacy of plant extracts in the control of storage insect pests in Nigeria. Nigerian Journal of Entomology. 2020;37(1):12–23.
[4] Ibrahim ND, Yusuf AU. Insecticidal activity of ethanolic extracts of local aromatic grasses against storage pests. Journal of Asia-Pacific Entomology. 2019;22(4):1105–1112.
[5] Ukwubile CA, Okoh OO, Abubakar MS. Bioactive constituents of Cymbopogon citratus leaves and their pesticidal properties. Phytomedicine Plus. 2021;1(2):100024.
[6] Ajayi OA, Adedire CO. Bioactivity of lemongrass essential oil and crude extracts against Acanthoscelides obtectus. Journal of Agricultural Science. 2021;13(2):88–97.
[7] Kumar AB, Sharma PK, Patel RN. Predictive modeling and dose optimization of botanical bio-insecticides using ensemble machine learning algorithms. Computers and Electronics in Agriculture. 2023;210:107920.
[8] Fernandes EO, Souza MA, Lima TH. Integration of environmental factors into machine learning frameworks for post-harvest pest control. Journal of Stored Products Research. 2024;105:102214.
[9] Adeleke MT, Akinneye JO. Phytochemical composition and toxicological evaluation of Cymbopogon citratus against legume bruchids. International Journal of Pest Management. 2019;65(3):210–218.
[10] Jeyakumar P, Tan W. Deep learning and ensemble machine learning applications in predictive automated toxicology. Computers and Electronics in Agriculture. 2022;194:106715.
[11] Wang CY, Liao HM. Machine learning mechanisms and regression tree modeling for biological and agricultural applications. Pattern Recognition Letters. 2023;165:45–52.
[12] Ekeh FN, Eze PM, Okoye CO. Botanical alternatives for the management of stored product insects in Nigeria. Bioresource Technology Reports. 2021;14:100667.
[13] Silva LM, Santos KR, Oliveira RC. Random Forest regressor optimization for non-linear bioassay dosage predictions in agricultural entomology. Pest Management Science. 2024;80(4):1892–1901.
[14] Olorunsola VO, Adebisi AA, Kolawole TT. Laboratory evaluation of botanical extracts in suppressing weevil reproduction. Journal of Crop Protection. 2021;10(2):133–142.
[15] Oni MO, Akinneye JO, Adedire CO. Comparative study of botanical extracts against maize weevil and cowpea bruchid. Journal of Agricultural Technology. 2022;18(1):55–68.
How to cite this paper
@article{1725006,
author = {Olotuah, R. T., Olotuah, O. F},
title = {Machine Learning-Driven Predictive Modelling for Laboratory Evaluation of the Toxicological Efficacy of Cymbopogon citratus Ethanolic Extract Against Acanthoscelides obtectus},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {5},
pages = {2593-2598},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1725006.pdf},
abstract = {Post-harvest storage losses inflicted by the bean weevil (Acanthoscelides obtectus) threaten agricultural economic stability and legume food security across Nigeria. While conventional pest management depends extensively on synthetic chemical treatments, issues surrounding environmental resilience, applicator health, and toxic residue deposition require eco-friendly botanical alternatives. This study evaluates the laboratory contact toxicity of an ethanolic leaf extract of Cymbopogon citratus (Lemongrass) against adult A. obtectus and introduces a machine learning framework to model time-dose-mortality dynamics. This is an advancement in smart farming that outperforms conventional linear calculations, especially when predicting pest control outcomes. Fresh C. citratus leaves were collected, air-dried, and subjected to cold maceration extraction using absolute ethanol. Gas Chromatography-Mass Spectrometry (GC-MS) analysis of the crude extract revealed a rich matrix of bioactive constituents, prominently dominated by citral isomers (geranial and neral), myrcene, and geraniol.
In controlled laboratory bioassays, adult bean weevils were exposed via filter-paper impregnation to graded volumetric doses of the ethanolic extract (0.0 to 10.0 ml). Empirical observations demonstrated strong, rapid contact toxicity, with a 10 ml dose inducing a 50% population mortality rate within 10 minutes, accelerating to complete population knockdown (100%) within 30 minutes, while untreated control cohorts experienced zero mortality. To expand these empirical thresholds into a predictive tool, an analytical dataset (n = 1,200) mapping extract dose, exposure intervals, temperature (25–30 °C), and relative humidity (60–75%) was evaluated using a Random Forest Regressor and Support Vector Regression (SVR). The Random Forest architecture yielded superior predictive performance (R2 = 0.988, MAE = 1.32%), isolating an optimal operational application benchmark of 6.8 ml to achieve near-total eradication (98.5%) within 45 minutes while minimizing active extract waste. This integration of botanical toxicology and ensemble learning provides a standardized, scalable framework for modern smart-storage pest management.},
keywords = {Acanthoscelides obtectus, contact toxicity, Cymbopogon citratus, predictive toxicology, Random Forest Regressor},
month = {November},
}