Develop user-friendly nutrient demand models

Integrating nutrient demand models and AI-based sensors with precision-dosing rigs to improve resource use and productivity, and reduce waste and emissions in commercial raspberry production


Soft fruit is an exciting product area with excellent growth potential. Although UK soft production is growing by ca. 8% a year,  demand for berries by UK consumers still exceeds supply. Continued growth is needed to displace often inferior imports,  but this must be achieved on a sustainable basis through efficient utilisation of valuable resources (primarily water and  inorganic fertilisers) and minimal environmental impact. 

Soft fruit growers know that a sub-optimal supply of macro- and micro-nutrients will limit marketable yields and berry  quality, but most guidelines on fertiliser inputs are hopelessly outdated. These formulations are often adjusted based on  anecdotal observations by growers and agronomists, but there is little scientific basis to these amendments and many  unneeded macro- and micro-nutrients accumulate in the substrate. Excessive N inputs often result in elevated emissions of N2O as a result of denitrification, and N2O emissions account for ca. 44% of the total agriculture-related GHG emissions. Reducing N inputs in agriculture and horticulture by  more closely matching demand with supply should help to reduce N2O emissions, but this is a risky strategy if guidelines  and monitoring sensors are not available. 

Our idea has potential to improve productivity and sustainability through improved resource use,  reduced waste and lower emissions. The initial benefits will be focused on the UK raspberry industry, but we anticipate that  our novel technology will enable and underpin similar advances in the wider fruit sectors other high-value horticultural  sectors (see below). We will actively engage with end-users throughout the project to deliver these objectives.

Project aims

NIAB combines new variety-specific N demand models with a prototype AI-based sensor that estimates NPK coir availabilities in real time, and embed the outputs into the NetBeat(tm) platform. The SmartNutrigation system will maintain coir NPK availabilities within a narrow optimum range during each developmental stage using outputs from nutrient demand models and real-time feedback from AI-based NPK sensors thereby maximising sustainability.

Exploitable outputs:

  1. Demonstration that NPK sensors can be integrated with an industry-leading irrigation control system
  2. Variety-specific N-demand models to target inputs more precisely
  3. A closed loop AI-based NPK optimisation tool
  4. Confidence to improve the NetBeatsystem with embedded N-demand models
  5. Objective and statistically-robust analysis of data sets to support commercial investment
  6. KE and demonstration activities at The WET Centre.

Project contact

Dr Mark Else






Project timing

August 2020 - February 2023