Opening — Why this matters now
Generative AI has already conquered the low-hanging fruit: emails, summaries, boilerplate code. The harder question is whether it can handle messy, domain-heavy science—where facts hide behind paywalls, nomenclature shifts over decades, and one hallucinated organism can derail an entire decision.
Agriculture is a perfect stress test. Pest management decisions affect food security, biodiversity, and human health, yet the relevant evidence is scattered across thousands of papers, multiple languages, and inconsistent field conditions. If AI can reliably translate this chaos into actionable knowledge, it could change farming. If it cannot, the cost of error is sprayed across ecosystems.
This study asks the uncomfortable question directly: What do today’s general-purpose LLMs actually know about sustainable crop protection?
Background — Context and prior art
Non-chemical pest management—biological control, botanicals, and agroecological practices—has decades of supporting evidence. Yet adoption remains limited. The reasons are familiar: fragmented literature, technical language, and advisory systems still biased toward chemical inputs.
Large language models promise to compress this complexity. Prior research shows LLMs can assist with literature screening and summarization, but most evaluations sit safely inside medicine or abstract benchmarks. Agriculture, with its ecological nuance and regional specificity, has been largely ignored.
The paper compares two archetypes of AI:
- ChatGPT (free-tier, non-grounded): trained on static data, strong at fluency, weaker at retrieval.
- DeepSeek (web-grounded): open-source, capable of live web access and structured reasoning.
The question is not which one sounds smarter—but which one knows more, knows it consistently, and knows when it doesn’t know.
Analysis — What the paper actually does
The author systematically interrogates both models on non-chemical control of nine globally significant pests, diseases, and weeds, across global and country-specific contexts (China, Indonesia, Thailand).
Each AI was asked to:
- Screen peer-reviewed literature
- Extract laboratory and field efficacy data
- Report averages, ranges, and variability
- List underlying biological agents or practices
Performance was judged on three axes:
- Breadth of knowledge — How much literature was screened, how many agents identified
- Internal consistency — Do lab and field results align in ecologically plausible ways?
- Factual reliability — Are agents, interactions, and citations real?
In other words: not just how much the AI says, but whether the story holds together.
Findings — Results that actually matter
1. Coverage: one model brought a library, the other a pamphlet
DeepSeek consistently screened 4.8–49.7× more publications and identified 1.6–2.4× more control agents than ChatGPT.
| Dimension | DeepSeek | ChatGPT |
|---|---|---|
| Literature corpus | Massive, multilingual | Narrow, inconsistent |
| Agents identified | Broad | Selective, sometimes missing entire categories |
| Country-level depth | Strong (esp. China) | Often thinner than global summaries |
ChatGPT’s free-tier outputs were frequently based on 80–98% fewer sources, raising a quiet but serious concern: accessibility tiers may directly shape epistemic quality.
2. Consistency: DeepSeek understands the lab–field gap (mostly)
DeepSeek’s reported field efficacy was systematically lower than lab results—exactly what real agronomy would predict. Pest identity and management tactic explained much of the variance.
ChatGPT’s results, by contrast, showed weaker correlations and higher noise. In some cases, field performance appeared almost detached from laboratory logic.
This matters because coarse realism is often more valuable than false precision.
3. Hallucinations: both models lie—confidently
Neither model escaped unscathed.
Common failure modes included:
- Invented biological agents (including non-existent viruses)
- Impossible experiments (field-only practices “tested” in laboratories)
- Taxonomic confusion (old and new species names treated as distinct)
- Selective blindness (entire guilds of predators omitted)
DeepSeek hallucinated more because it said more. ChatGPT hallucinated less—but also omitted critical knowledge.
The trade-off is stark: breadth increases both insight and risk.
Implications — What this means beyond agriculture
This paper is not really about pests. It is about AI as an epistemic actor.
Three broader implications stand out:
- Web-grounding matters — Live retrieval dramatically expands coverage, especially in non-English science.
- Free-tier AI may quietly widen the knowledge gap — Not just between users, but between regions.
- LLMs are pattern detectors, not truth engines — They reliably capture trends, not ground truth.
For farmers, coarse-grained guidance may already be useful. For scientists and policymakers, unsupervised AI synthesis remains dangerous.
The real opportunity lies in human–machine collaboration, where AI handles scale and humans handle judgment.
Conclusion — Automation with adult supervision
This study lands on an unfashionable but necessary conclusion: today’s LLMs are neither saviors nor scams.
They are powerful, biased, occasionally delusional tools—remarkably good at seeing the forest, unreliable with the trees. Used carefully, they can democratize access to neglected scientific domains like agroecology. Used blindly, they risk automating misinformation at scale.
In short: let the AI read everything. Don’t let it decide alone.
Cognaptus: Automate the Present, Incubate the Future.