Enterprise AI Analysis
General-purpose AI models can generate actionable knowledge on agroecological crop protection
Generative artificial intelligence (Al) offers potential for democratizing scientific knowledge and converting this to clear, actionable information, yet its application in agri-food science remains unexplored. Here, we verify the scientific knowledge on agroecological crop protection that is generated by either web-grounded or non-grounded large language models (LLMs), i.e., DeepSeek versus the free-tier version of ChatGPT. For nine globally limiting pests, weeds, and plant diseases, we assessed the factual accuracy, data consistency, and breadth of knowledge or data completeness of each LLM. Overall, DeepSeek consistently screened a 4.8-49.7-fold larger literature corpus and reported 1.6-2.4-fold more biological control agents or management solutions than ChatGPT. As a result, DeepSeek reported 21.6% higher efficacy estimates, exhibited greater laboratory-to-field data consistency, and showed more realistic effects of pest identity and management tactics. However, both models hallucinated, i.e., fabricated fictitious agents or references, reported on implausible ecological interactions or outcomes, confused scientific nomenclatures, and omitted data on key agents or solutions. Despite these shortcomings, both LLMs correctly reported low-resolution efficacy trends. Overall, when paired with rigorous human oversight, LLMs could support farm-level decision-making and unleash scientific creativity.
Authored by Kris A. G. Wyckhuys, School of the Environment, University of Queensland, Saint Lucia, Australia
Executive Impact: Key Findings
Generative AI models like DeepSeek and ChatGPT offer transformative potential for agroecological crop protection by democratizing scientific knowledge. DeepSeek, a web-grounded model, demonstrated significant advantages over ChatGPT's free-tier version, screening a vastly larger literature corpus (4.8-49.7x more) and identifying more management solutions (1.6-2.4x more), leading to 21.6% higher efficacy estimates and better data consistency. While both models showed a propensity for hallucination and data omission, they accurately captured low-resolution efficacy trends. With human oversight, these LLMs can significantly enhance farm-level decision-making and fuel scientific innovation in sustainable agriculture.
4.8-49.7x
Larger Literature Corpus (DeepSeek)
1.6-2.4x
More Solutions Identified (DeepSeek)
21.6%
Higher Efficacy Estimates (DeepSeek)
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98.0%
Smaller Literature Base for P. xylostella (ChatGPT vs DeepSeek)
| Feature | DeepSeek | ChatGPT |
|---|---|---|
| Literature Corpus Size | 4.8-49.7x larger than ChatGPT | Significantly smaller (e.g., 98.0% less for P. xylostella) |
| Solutions Identified | 1.6-2.4x more biological control agents | Fewer solutions, sometimes omitting entire guilds (e.g., P. xylostella predators) |
| Efficacy Estimates | 21.6% higher average efficacy | Lower and more variable efficacy estimates |
| Lab-to-Field Consistency | Greater consistency | Higher variability in lab-to-field differences |
| Factual Accuracy | Generally good, but some hallucinations (e.g., B. tabaci NPV) | Hallucinations common, fictitious agents/references |
| Scientific Nomenclature | Poor distinction between old/new terms (e.g., Paecilomyces/Isaria) | Confused scientific nomenclatures |
The Challenge of Hallucination: False Efficacy Reports
Both DeepSeek and ChatGPT exhibited instances of hallucination in insect pest management. For example, either bot reported up to 65-87% B. tabaci control efficacy by Nosema bombycis and Spodoptera exigua nucleopolyhedrosis virus (NPV), even though neither microorganism has ever been reported to affect this pest. DeepSeek also 'hallucinated' non-existent agents like 'B. tabaci NPV', despite no DNA viruses being known from this pest. These fabrications highlight the critical need for human oversight to verify AI-generated knowledge.
90-93%
Peak Efficacy for Disease Control (Botanicals & Agroecology)
| Aspect | DeepSeek Performance |
|---|---|
| Literature Coverage |
|
| Efficacious Solutions (Diseases) |
|
| Efficacious Solutions (Weeds) |
|
| Factual Accuracy |
|
| Consistency |
|
Unverified Efficacy Claims in Disease & Weed Management
DeepSeek, while comprehensive, still presented unverified efficacy claims in disease and weed management. For instance, it reported superior efficacy for Pseudomonas fluorescens or Paenibacillus polymyxa against Phytophthora infestans, which could not be corroborated by published records. Similarly, high field efficacy claims for turmeric extract against wheat rust and Fusarium spp. lacked full corroboration. This underscores the challenge of 'fact-checking' AI-generated summaries, as even plausible-sounding data can be misleading without direct scientific validation.
Enterprise Process Flow
Initial records identified
→
Duplicates removed
→
Records screened
→
Excluded at title/abstract level
→
Full-texts assessed
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Excluded with reasons
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Studies included
175%
Max Data Disparity (DeepSeek vs ChatGPT on Studies Included)
| Aspect | DeepSeek | ChatGPT |
|---|---|---|
| Initial Records Identified (B. tabaci, China) | 837.4 ± 303.3 records | 381.0 ± 359.5 records (45.5% of DeepSeek's) |
| Final Studies Included (B. tabaci, China) | 135.6 ± 31.2 records | 22.6 ± 23.9 records (16.7% of DeepSeek's) |
| Literature Sources | Broader set, including Chinese language databases (Wanfang, CQVIP) | 83% fewer literature sources, hints of restricted ISI access |
| Data Disparity (Microbials/Agroecology) | Lower disparity for invertebrate BCAs (12-17%) | Higher disparity (172-175% for microbials/agroecology) |
| PRISMA Steps Consistency | Consistent across all seven steps | Disparity varied across pest management tactics |
| Potential Limitations | Occasional 'hallucination' of agents/references | Free-tier possibly defaults to older models for complex queries, higher risk of hallucination |
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Your AI Implementation Roadmap
A typical journey for integrating AI solutions into enterprise workflows. Each phase is tailored to ensure maximum impact and seamless adoption.
Phase 1: Needs Assessment & Pilot
Identify specific crop protection challenges, evaluate existing data infrastructure, and deploy small-scale AI pilot projects with expert human oversight.
Phase 2: Data Integration & Model Training
Consolidate diverse agricultural datasets, train general-purpose AI models on relevant agroecological knowledge, and fine-tune for specific crop-pest scenarios.
Phase 3: System Deployment & User Training
Integrate AI decision-support tools into farm management systems. Provide comprehensive training for agronomists and farmers on responsible AI use and verification.
Phase 4: Performance Monitoring & Iteration
Continuously monitor AI recommendations against field outcomes, collect feedback, and iterate on models to improve accuracy, consistency, and reduce hallucination.
Phase 5: Scaling & Enterprise Integration
Expand AI solutions across larger agricultural operations or regions, integrating with broader sustainable food system initiatives and policy frameworks.
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