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Mutus Tech

Resources and publications

Product briefs, peer-reviewed papers, project notes, datasets and technical explainers.

A working library of what Mutus Tech publishes — for farms and advisers evaluating the platform, for academic and project partners and for journalists and programme funders.

Product briefs

What we sell, summarised.

  • Sustainable Fertiliser Management

    How Parallel Farm threads NPKS planning, N-rate sensitivity, Farm Tasks and applied-input records on one field polygon.

    Read the brief →

  • AI Adaptive Pest Management

    How Pezego turns a field photo into a region-specific pest identification and IPM action plan.

    Read the brief →

  • Platform & Technology

    The mobile-cloud architecture, AI model services and data pipelines that underpin every Mutus Tech module.

    Read the brief →

Downloadable PDF product sheets are in preparation — contact us if you need one for procurement or programme submission.

Research publications

Peer-reviewed work.

  • IEEE Internet of Things Journal · 12(23)

    PEZEGO — AI-powered pest identification and climate-smart IPM

    Co-developed with RSK ADAS, University of Sheffield and University of Liverpool. 20+ farms, 10,000+ annotated pest images, 38 pests and 10 crops in the UK dataset.

    DOI 10.1109/JIOT.2025.3586374 →

  • IEEE INDIN 2025

    DA-Mamba — state-space N₂O prediction model

    A peer-reviewed environmental-performance modelling basis integrated as a Parallel Farm microservice. University of Sheffield + Mutus Tech Ltd. Calibration continuing through project and pilot partners.

    DOI 10.1109/INDIN64977.2025.11279073 →

Project notes

Active programmes and historical R&D.

  • 2024 – 2026

    Climate-Smart Fertiliser consortium

    UKRI / Innovate UK-backed · £710K+ total funding award. Multi-partner project calibrating field-level nutrient planning and climate-smart practice with academic and industry partners.

    More on this →

  • Ongoing

    BridgeAI · Innovate UK programme

    External programme support for AI scale-up and technical collaboration in agri-food. Includes BridgeAI Annual Showcase participation.

    More on this →

  • March 2025

    UK–India Agri-Tech Accelerator

    Selected as one of five UK agri-tech companies for the knowledge-exchange programme; visits to Delhi, Hyderabad and Bengaluru.

    More on this →

  • 2021

    Historical R&D projects

    Earlier Innovate UK-funded work that informed the current platform — Feasibility Analysis (China) and Mobile Soil Health (Sheffield + ADAS).

    More on this →

Datasets

Data resources underpinning our models.

Datasets used for research, model development and pilot calibration. Field recommendations remain subject to agronomic context and partner validation.

Technical explainers

Short notes on how the platform actually works.

  • How NDVI works as a crop-response signal

    NDVI compares red and near-infrared light reflected from plant leaves. Healthy canopies absorb red and reflect near-infrared strongly — producing higher values. We use Sentinel-2 Copernicus imagery, revisited every ~5 days (subject to cloud cover). NDVI is not a direct soil-health measurement; soil-health interpretation combines NDVI with soil tests, crop context and management records.

  • How AHDB RB209 N-rate sensitivity checks work

    The AHDB Fertiliser Manual RB209 sets recommended N rates based on crop type, target yield, soil-nitrogen supply, previous cropping and soil mineral-nitrogen status. Our N Calculator implements the V08 formulae and exposes the same parameters. Economic sensitivity checking takes that agronomically derived rate and recomputes financial impact at current grain and fertiliser prices — supporting an informed decision, not replacing agronomic judgement.

  • How field task records support traceability

    Every Farm Task carries the field polygon, recommended rate, any adjusted rate, assignee, due date and application notes. When applied, the operator logs completion date, applied rate, photos and a weather snapshot. Because the same polygon is the unit of work at every stage, plans, tasks, applied inputs, NDVI scans and weather context all sit on one record — ready for partner review without after-the-fact reconstruction.

  • How soil-health interpretation combines soil tests and crop response

    Soil health is multi-parameter — chemistry (pH, organic matter, available P/K/Mg, S), physical structure, biology and crop response over time. The platform brings together what each measurement can show: soil tests for the chemical baseline; Climate History for seasonal context; NDVI Analysis for crop response; Records for management decisions. No single signal is a verdict — but together they support an informed adviser conversation.

Need something specific?

Looking for a product sheet, dataset sample or research brief?

We share product briefs, dataset samples and project methodology documents on request — typically with academic, programme or supply-chain partners.