ReviewPest management science2025
Artificial intelligence in the mass production of natural enemies for biological control in modern agriculture.
Review in Pest management science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Arthropod Natural Enemies in Biological Control: A Systematic Bibliometric Analysis 2016-2025.Insects · 2026Article
- Identifying systemic risks and mitigation strategies of artificial intelligence in agriculture: from social-technical-ecological systems framework.Frontiers in plant science · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The increasing challenges posed by pest infestations in contemporary agriculture demand sustainable alternatives to conventional pesticides. Biological control (BC), which utilizes natural enemies (NEs) such as predators and parasitoids, offers an eco-friendly approach to pest management. However, large-scale production of NEs faces significant challenges, including high costs, time-consuming processes, and inconsistent quality. Artificial intelligence (AI) is increasingly being applied in modern agriculture to enhance the efficient mass production of NEs for BC. By leveraging automation, machine learning (ML), computer vision, and real-time data analytics, AI can improve key aspects of NEs production, including diet formulation, environmental control, behavioral monitoring, and quality assurance. AI-driven systems promote consistency and scalability in NE manufacturing, while adaptive feedback mechanisms enable continuous process optimization. Furthermore, AI supports the development of predictive models and customized distribution strategies, ensuring the timely and effective deployment of NEs. Despite challenges such as high initial investment and limited data availability, the integration of AI in NEs production holds considerable promise for cost-effective, scalable, and sustainable BC strategies. This review explores the intersection of AI and BC, highlighting current applications, key challenges, and future opportunities in AI-enhanced BC and NEs mass production. It synthesizes recent advancements and identifies research gaps, providing a comprehensive overview of AI's evolving impact on crop protection. By optimizing NE production and reducing dependence on chemical inputs, AI contributes to improved biodiversity, alignment with global sustainability goals, and a more resilient agricultural future. These insights are valuable for researchers, practitioners, and policymakers working toward sustainable and inclusive pest management solutions. © 2025 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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.