We understand that the pressure on farmers is multi-faceted and intense

Economic Strain: The surge in energy and fertiliser prices, labour rate inflation and increased payroll taxes have resulted in intense financial pressure.

Labour Shortages: Finding and retaining harvest labour is a common challenge, particularly during peak season.

Sustainability Pressures: Pressure to ensure that agricultural commodities come from sustainable sources in order to prevent biodiversity loss and deforestation along with increasing consumer demand for demonstrably sustainable products has increased pressure on farmers.

Climate Challenges: Unpredictable weather patterns, such as droughts, floods, and extreme temperatures, can devastate crops, adding to the stress.

Regulatory Changes: New regulations, including those related to Brexit, have added complexity and costs of compliance. The UK Government’s Net Zero Strategy aims for net zero carbon emissions by 2050, with the food and drink sector targeting 2040. This involves reducing direct Scope 3 emissions, which can be particularly challenging and will require farmers to engage.

We have a patented Automated Selective Harvester ('Harvesta') that transforms the economics of broccoli harvest.
Currently broccoli is harvested by hand, making it an expensive vegetable due to the high cost of harvest, partly as a field will require several passes by a large team of farm workers. We change that, and in addition we allow the nutritious stalk to be taken off the field so we can process it using our patent-filed process to produce our Prota, Fiba, Necta and Ova. By making harvest less costly; reducing crop lost due to insufficient labour; and sharing the value from the side-stream, we improve the financial sustainability of farmers.

In addition, we use machine learning approaches (a subset of Artificial Intelligence) to select which heads to cut on each pass, which means we image every plant on each pass.
As a result, we rapidly build up a unique picture of the field that we are turning in to a data portal ('Intellia') that will give insight to support farmers in extending 'precision agriculture'. Using the 'big data' we generate, we aim to supply farmers intelligence they need but do not have, allowing insight that will help them to optimise their harvest activity and manage their crop and land better, further improving efficiency.

Our services can help farmers address labour and profitability issues

Cost reduction: We offer the ability to reduce the requirement for harvest labour, helping farmers ensure that a high-value crop is also a high-margin crop. Automating harvest means that farmers can take crop off the field when temperatures are lower, reducing the energy cost of the initial chilling.

Improved yield: We can help farmers improve yields by reducing losses due to lack of labour availability and reducing unharvested crop.

Utilising un-harvested crop: If there is a glut, farmers may be leaving crop on the field. We can use that crop giving some return, even if it not all they want or deserve.

Monetize what is currently wasted: Farmers will share in the upside of monetizing the side stream (stalk and stem) that is often currently left to rot.

Better data: We can utilise the data we acquire to automate the harvest to help farmers make better informed decisions.

Image above is a development product, service will not be live until H2 2025.

Model development: We use Machine Learning approaches to select heads for harvest. These approaches are embodied as models that are deployed on-board the Harvesta.

Variety specific: We have training data from four main varieties and we are optimising our models to accommodate them. More varieties will follow.

Optimisation: We aim to work with universities to produce ever-better models which can be offered to farmers on an "AppStore" basis.

Customisation: Ultimately, we aim to train variety-specific models that are optimised to each region, eventually farm, to continuously refine selection, forecasting and crop health / disease / pest reporting.