A new government-funded scheme in the UK is seeking to apply IoT sensing and AI sense-making to lamb farming. The UK Agri-Tech Centre, an independent organisation for agricultural innovation, is working with farm equipment manufacturer David Ritchie Agricultural (Ritchie) to prototype an automated IoT/AI system to weigh livestock to monitor growth rates and provide guidance about feeding, grazing, breeding, and sales.
The group proposes three different designs: a walk-on weigher, a creep-feed system, and a climb-on platform. The creep-feed is to be metered to prevent “dominant animals getting more than their share of feed”, and restrict intake to prevent over-feeding and ill-health. The climb-on platform is designed to utilise a lamb’s ‘play instinct’. The upshot will be reduced labour for farmers (by making weighing “less stressful and time-intensive”) and better welfare for their livestock.
The proposed solutions use ‘load cells’ to measure weight (of livestock and feed). These are to be fitted with microcontrollers to collect the data, and attached to electronic identification (EID) tags to take transfer readings. Public 4G/LTE networks (“using a mobile modem, also capable of local-area and Wi-Fi connectivity”) will be used to carry data from the sensors / equipment to the cloud. A data-handling app is being developed to work with all three designs.
A spokesperson for the UK Agri-Tech Centre said: “In remote areas, data will be stored on-board then downloaded periodically to a mobile device. We are currently doing trials with near-field communication (NFC) and Bluetooth Low Energy (BTLE). Our database will filter and process data. Animal group data will be presented to allow monitoring of average daily live weight-gains – giving indications of diet performance and flock health.”
Individual animal growth history will also be available to pick out better and worse performing lambs, said the spokesperson. Ritchie, a specialist in grassland and livestock equipment, will construct the prototypes and commission them at one of its own trial farms, along with three satellite farms belonging to the UK Agri-Tech Centre, for validation. So-called “second-stage” hardware prototypes will be deployed this month (February).
These will be trialled over the summer; validation of the data insights follow. A website will be constructed as well, apparently, and tested by farmers and refined accordingly. The trial period ends in September of next year (2026). The project, called ‘Lamb Monitor’, is funded by the UK Department for Environment Food and Rural Affairs (Defra) and Innovate UK, via its Transforming Food Production (TFP) ‘challenge programme’.
Scottish duo E-Matters Consulting and TayFusion, an industrial-change consultancy and database system developer respectively, are also involved.
A press note quoted research (CIEL, 2020; Morgan-Davis, 2018) that suggests IoT-assisted precision farming can dramatically reduce greenhouse gas (GHG) emissions (“two hill farms had a 68 percent and 49 percent reduction”) and manual workloads (“between 19 percent and 36 percent labour [at] £1.60-£3 per ewe, depending on the farm”). It said these results “did not include auto-weigher(s), just performance recording using EID and software packages”.
Charlie Brown, product development manager at Ritchie, said: “We hope to bring some level of automation and improved efficiency to the sheep industry with this product. The first prototype was created after farmer-led interactions resulted in us making efficiencies within the industry. Following field trials and farmer feedback we are refining the product for our (second-stage) trial of the prototype.”
Hayley Gerry, project manager at the UK Agri-Tech Centre, said: “This project has enabled the UK Agri-Tech Centre to bridge a gap that the industry has identified where efficiencies could be made.
Using our farmer network, we are able to test and validate the prototypes in a commercial set up to allow for feedback from the farmers. This will allow Ritchie to commercialise a product that will make farming more efficient.”
