CropMonitor Pro

CropMonitor Pro is a state of the art sophisticated decision support platform which has been developed by Fera with Crop Health and Protection funded by Innovate UK. Services to support decisions at the local (field) scale was launched for wheat and oilseed rape crops in September 2020. Landscape scale weather feed from UKMO which is a 2km x 2km gridded weather dataset for current and forecast weather delivers local weather services for each registered field location.

  • Risk prediction platform (generic)

Daily pest risks (current day risk relative to threshold) for individual crops using Crop Monitor weather services, based on location, agronomic information and pest observations.

  • Weather and environment feed

Current and forecast weather, soil type

  • Crop details and development

Agronomy, growth stage prediction

  • Pest/pathogen prediction

based on more than 30 models

  • Validation platform

Trials networks

Performance of models have proven high levels of accuracy

  • Decision support module

Spray/not spray, timing, weather suitability

  • Website access now available

Free service at regional level

Subscription service to field scale

3rd party API access in development and available on request


Hourly outputs for current day and forecasts in three hourly intervals for the following four days.

Current day accumulated risk score for pests and diseases to identify need to spray. Support with decision on whether to spray and which pests/diseases to target.  Current day high, medium or low accumulated risk.

Spray scheduler to understand whether current day is approaching/within appropriate spray timing window.


Participatory sharing of pest and disease sightings to improve risk alerts. Submitted via CropMonitor Pro application via desk top, laptop, tablet or phone.

Facilitated access to monitoring services which can inform spray thresholds to further support spray decisions.

This information is then available for farmers and growers to enable them to make more effective decisions about the treatments they need to apply to their crops in order to improve the quality and quantity of their yields.

CropMonitor provides information sourced from monitoring sites located across the country and reports up to date measurements of crop pest and disease activity in arable crops throughout the UK. Samples, collected from these monitoring sites are also used for analysis and research by other services within CHAP: The National Reference Collection and Molecular Diagnostics Laboratory.


Bespoke local forecasts applicable for supporting instant pest and disease management decisions available via a subscription service.

Our services include:

National Weather Monitoring Network

  • UKMO weather data at a regional scale.
  • 30 bespoke met stations report data to support UKMO data
  • Met data used in models developed to predict pest and disease risks.
  • Collaboration with the UK Met Office to identify an agriculturally relevant national UKMO monitoring network to support real-time risk forecasting services for agriculture and horticulture.
  • Provision of weather forecast (up to 14 days ahead).

Modelling Platform for Pest and Disease Forecasting

  • Decision Support System with modules for a range of pests and diseases in winter wheat, oilseed rape and potatoes
  • Red – Amber – Green daily alerts provide advice on the need to spray or not spray
  • Spray timings on the farm can be planned based on risk and predicted spray window
  • Uses local weather forecast available for 2 km x 2 km grids allows optimal spray timing
  • Validation platform – supporting further research with trials network linking demonstration sites
  • Independent validation of scientific models
[Sassy_Social_Share style="background-color:transparent;" count="1" total_shares="ON"]

For any further information about this capability or to discuss a collaboration and/or grant for a commercially funded project, complete the form below.

    I would like to sign up to the CHAP eNewsletter.

    [activecampaign form=7 css=1]