Is Fotenix right for you? Building a business case for crop-level diagnostics

At a glance

Across controlled-environment deployments, crop-level diagnostics with Fotenix have delivered a fivefold increase in labour efficiency by reducing routine monitoring time and cutting detection by up to 80%.

Earlier detection also reduces the need for whole-block interventions, limits unnecessary input use, and lowers the risk of quality downgrades that can cost around £300 per hectare on average.

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In controlled-environment systems, where energy, water, and labour account for the majority of operating costs, each unnecessary pass through the crop adds to overheads. Automated monitoring helps remove that inefficiency, allowing teams to assess crop performance from the control room rather than the greenhouse.

But is it right for you?

If you’re trying to determine whether crop-level diagnostics could have a meaningful impact on your bottom line, the best place to start is with the inefficiencies you already know about. 

labour 

For most operations, labour is the most visible operating cost and has the clearest correlation with performance and output. Routine monitoring, grading, and quality checks are time-intensive, and most growers will have an accurate idea of how many hours are spent on each. 

To establish a baseline, start with three simple questions:

• How many hours are spent each week on routine crop monitoring per hectare?

• How much of that time is spent confirming that nothing has changed?

• What is the average hourly cost of staff? 

  
  

In Fotenix deployments, we expect around x5 increase in labour efficiency. Assuming 10 hours of monitoring per hectare a week, that's around six hours saved per hectare. It's a sizeable resource to redeploy to other, high-value activities.

interventions 

When it’s unclear where a problem started or how far it has spread, interventions tend to be broader than intended. Whole-block treatments become the safer option, bringing with them higher labour input, increased chemical use, and additional follow-up work. If you can identify issues before they become widespread, you can treat them more efficiently.

A review of recent interventions helps to understand any existing inefficiencies:

• How often is a whole block treated 'just to be safe'?

• How much labour is involved in preparation, application, and follow-up?

• What was the impact on yield forecast or quality?

  
  

Earlier detection allows responses to be limited to affected areas. Fewer plants are disturbed, fewer inputs are applied, and less corrective work is needed later in the cycle.

Fotenix has been shown to reduce the time taken to detect issues in controlled environments by up to 80%. Giving growers earlier visibility and reducing the need for blanket treatments and overcorrection, as well as the labour and input costs associated with it. 

Consider your recent interventions. What would it mean to your bottom line to identify an issue at day 2, rather than day 10?

Quality protection: reducing avoidable loss 

Quality risk is harder to recover from than yield loss. Once shelf-life or uniformity is compromised, the commercial outcome is often fixed.

When building a business case, consider where quality issues tend to surface in your operation:

• How often do quality problems appear late, when there is little room to respond?

• How regularly does saleable volume come in under forecast?

• What's the typical cost of downgrades?

If you could detect and rectify issues earlier in the growing cycle to avoid downgrades or improve shelf-life and uniformity, what would it be worth?

Improving forecasting accuracy 

Inaccurate forecasts carry real costs. Overestimating supply creates a surplus that has to be sold cheaply or wasted. Underestimating supply means buying in at short notice to fulfil contracts, often at a premium. Both scenarios undermine margin.

Continuous, crop-level monitoring improves forecasting accuracy by providing earlier, more reliable insight into crop health and development. That gives commercial teams more time to adjust harvest plans, volumes, and commitments, limiting the cost of late corrections.

When assessing value, consider the downgrade risk, as well as the operational and commercial impact of getting forecasts wrong.

Bringing it together

For most operations, adopting crop-level diagnostics is less about technology and more about confidence. Confidence that issues are being caught early. Confidence that labour is being used where it adds most value. And confidence that quality issues will be detected accurately, early in the cycle. 

When labour time is tight, interventions carry real cost, and quality and forecasting errors are hard to recover from, that certainty matters. It’s what allows teams to trust that time is being spent where it makes a difference, and that problems are being addressed before they become expensive.

If you want greater confidence in how efficiently your operation is running and in the decisions being made day-to-day, the Fotenix team can help you work through what earlier visibility would mean in practice. 

To discuss how crop-level diagnostics could help bolster your bottom line

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