ReviewHeliyon2024
Systematic approaches to machine learning models for predicting pesticide toxicity.
Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 31 citations in OpenAlex.
- Flavonoids as Antimicrobial Pesticides: Mechanisms of Action, Formulation Strategies, and Perspectives for Pre- and Postharvest Disease Management.Plants (Basel, Switzerland) · 2026Review
- Review
- Predictive modeling of millet growth in pesticide- and vinasse-amended soils using SHAP regression interpretation.Scientific reports · 2026Article
- Building Trust: Validating Artificial Intelligence (AI) and Machine Learning in Agricultural Applications.Journal of agricultural and food chemistry · 2026Article
- Structure Design and Performance Study of Bionic Electronic Nasal Cavity.Biomimetics (Basel, Switzerland) · 2025Article
- A predictive framework using advanced machine learning approaches for measuring and analyzing the impact of synthetic agrochemicals on human health.Scientific reports · 2025Article
- Article
- Fertilization for Growth or Feeding the Weeds? A Deep Dive into Nitrogen's Role in Rice Dynamics in Ecuador.Life (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.