The global biopesticides market reached $8.9 billion in 2025 and is projected to hit $17.7 billion by 2030, growing at 14.6% annually (MarketsAndMarkets, 2025). R&D teams at biologicals companies are racing to bring new biocontrol agents to market. But they all hit the same wall.
Before you can design a field trial, you need to know what the existing literature says. Under what conditions does this biological agent work? Where does it fail? Where has nobody looked? That literature synthesis, the foundation of every R&D programme, takes months of manual work.
We built a research engine that does it in hours.
The problem R&D teams face
Biological crop protection is fundamentally different from chemical pesticides. A chemical molecule works or it doesn't, largely independent of environmental context. A biological agent, a living organism, is sensitive to everything: temperature, humidity, UV exposure, soil microbiome, application timing, product formulation, pest life stage.
That makes efficacy conditional. And it makes the research landscape complex.
A single biocontrol agent like Trichoderma harzianum has over 300 molecularly characterised species, each with different efficacy profiles across crop-pathogen combinations. Published studies span dozens of countries, multiple languages, and decades. Some show 80% disease suppression. Others show 20%. Others show no effect at all. The difference often comes down to conditions that weren't the focus of the study.
The numbers behind the literature bottleneck are stark. A systematic review in agricultural science costs an average of $141,000 and takes 67 weeks to complete. Database searches return over 5,500 documents on average, of which less than 4.7% turn out to be relevant. And the corpus keeps growing: biocontrol publications increased from around 5,000 per year in the early 2000s to over 20,000 per year by 2019, a fourfold increase in a decade.
R&D teams do this work manually. Scientists reading papers, building spreadsheets, trying to reconcile contradictory results. It works, but it doesn't scale.
The stakes
Developing a new crop protection product costs an average of $307 million and takes 11.4 years from discovery to market (CropLife International / AgbioInvestor, 2026). Biological products are faster and cheaper, but the regulatory path is still significant. An EPA biopesticide registration takes under 11 months on average, but EFSA active substance approval takes 2.5 to 3.5 years, with up to another year for individual member state authorisation.
Biologicals now represent 10% of crop protection R&D investment globally. Companies are pouring resources into this space:
- Corteva invested $1.4 billion in R&D in 2024 and generated $476 million in biologicals revenue. Their target: $2 billion by 2035.
- BioFirst (formerly Biobest + Biotrop) reaches approximately EUR 500 million in revenue, focused entirely on pollination, beneficial insects, and biopesticides.
- Koppert generated EUR 417 million, 100% from biocontrol.
- Bayer targets $2.3 billion in biologicals revenue by 2030, up from roughly $200 million in 2022.
The top 8 biologicals companies control only 35% of the market. It's one of the most fragmented sectors in agriculture. That fragmentation extends to the research: efficacy data is scattered across disparate methodologies with no unified metrics or standardised reporting.
What Truleaf.org built
Our research engine synthesises peer-reviewed academic literature into structured, queryable profiles for biological crop protection agents.
Give it an organism, a target pest, and a crop. The engine searches across academic databases and languages, extracts performance data, maps the conditions under which the agent works, and identifies where the literature has gaps.
The output is a conditional-efficacy profile: a structured view of where your biological agent is effective, where it's marginal, where it's ineffective, and where nobody has tested it yet. Every data point is provenance-locked to its peer-reviewed source.
What it produces
Conditional efficacy mapping. Not just "does it work?" but "under what specific conditions does it work?" Temperature bands, humidity ranges, UV exposure, product format, application method, pest life stage. The engine maps performance across these dimensions, turning fragmented papers into a queryable matrix of organism, pest, crop, and condition.
Whitespace identification. The combinations that the literature hasn't covered. If nobody has tested your Beauveria bassiana strain under high-UV field conditions in Mediterranean climates, the engine surfaces that gap. These whitespace maps tell R&D teams exactly where to focus their next field trial.
Provenance-locked data. Every claim, every number, every performance figure traces back to its source paper. This isn't a summary generated from thin air. It's structured evidence that you can inspect, verify, and cite. All data is export-ready for regulatory dossiers.
Why this matters now
The biologicals industry is at an inflection point. The EU's Farm to Fork strategy targets a 50% reduction in chemical pesticide use by 2030. Global demand for biological crop protection is growing faster than any other segment in agriculture.
But the R&D bottleneck remains. A significant portion of development time is spent on literature synthesis: understanding what's already known before investing in new field trials. With the biocontrol literature growing at 21% annually (2022-2024 average), the manual approach falls further behind every year.
AI is already transforming other parts of crop protection R&D. Enko Chem's ENKOMPASS platform claims 75% faster molecule discovery. AgBiome (now part of Ginkgo Bioworks) built the world's largest sequenced microbial collection with 115,000+ strains. These tools accelerate discovery. What was missing was a tool that accelerates the literature synthesis that comes before discovery decisions.
That's what Truleaf.org built. The engine compresses the literature synthesis phase from months to hours, without sacrificing rigour. The data is structured, sourced, and auditable.
For R&D teams, this changes the economics. You can evaluate more candidates faster. You can identify the most promising whitespace earlier. You can build stronger regulatory dossiers with comprehensive evidence coverage. And you can make field trial decisions based on a complete view of the literature, not a partial one.
Who this is for
Biologicals R&D teams deciding which biocontrol agents to develop and where to focus field trials. The conditional-efficacy profiles and whitespace maps answer the question: "Where should we test next?"
Regulatory affairs teams compiling efficacy data packages for EFSA, EPA, or other regulatory bodies. The provenance-locked data structure maps directly to the evidence requirements in registration dossiers.
University research groups studying biological crop protection. The engine surfaces where studies disagree and identifies the conditions that might explain why.
See it in action
We're offering demos to R&D teams working in biological crop protection. We'll run the engine on your target organism and show you the conditional-efficacy profile and whitespace map.
If you're deciding what to test next in the field, this tells you where the literature stands before you design the trial.
Reach out at truleaf.org/research-and-development.