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Yield Optimization

Valvix AI analyzes your complete farm history and suggests specific, data-backed changes to growing conditions — so every cycle performs better than the last.

Yield Optimization
22%
Average yield improvement
Season 2
When optimization effects are strongest
100%
Based on your own farm data

The Problem

Most growers know their yields could be better — but don't know exactly what to change. Generic agronomic advice ignores the specific conditions of your farm, your equipment, and your local climate. Improving yield requires understanding what actually drove your best results.

Generic growing guides don't account for your specific conditions

Good harvests are hard to replicate because the reasons aren't documented

Trying new approaches risks an entire cycle without certainty of improvement

No way to test the effect of a single variable without a controlled experiment

How Valvix Solves It

1

Valvix correlates every input with harvest outcomes

Temperature, humidity, irrigation, EC, light, and timing data from each cycle is linked to the yield data you enter at harvest.

2

AI identifies the conditions that produced your best results

Statistical models find which environmental parameters correlated with above-average yields in your specific history.

3

Optimization suggestions are ranked by expected impact

Valvix shows specific recommendations — 'Increase nighttime EC by 0.2 in zones 2–4' — with the evidence from your own data that supports each one.

4

Changes are applied as new target parameters

Approved suggestions can be applied directly to zone settings — Valvix will maintain the new targets automatically.

5

Results are tracked and the model updates

At the next harvest, actual results are compared to predictions. The model learns from the outcome and refines future suggestions.

Real Installations

Yield Optimization Yield Optimization Yield Optimization

Yield optimization results across vegetable, herb, and berry operations

See It in Action

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Watch: Yield Optimization Demo

3 min overview · Real farm footage

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What You Get

22% average improvement

Measured across farms in their second season using Valvix optimization

Evidence-backed suggestions

Every recommendation links to the specific data from your history that supports it

Single-variable analysis

Understand the effect of changing one parameter while holding others constant

One-click application

Approved suggestions apply directly to zone targets — no manual reconfiguration

Continuous refinement

The model updates every cycle, getting more accurate as your history grows

Cycle comparison

See yield, quality, and input cost side by side across all your growing cycles

What Growers Say

Marta S.

"Valvix found that our best tomato cycles all had one thing in common — a slightly lower night temperature in the last two weeks. We'd never noticed it. Applied it deliberately this season — yield up 19%, sugar content noticeably higher."

Marta S.

Tomato greenhouse · 5 ha · Kharkiv

Bohdan K.

"I was skeptical that software could tell me how to grow better. Then it showed me that my best cycles correlated with EC levels 0.3 higher than my standard. Tried it — immediately saw the difference in plant vigor."

Bohdan K.

Pepper farm · 3 ha · Odesa

Yana H.

"The optimization suggestions paid for two years of Valvix in one season. We found we were underfeeding our basil by about 15% in the second half of every cycle. Simple fix, significant result."

Yana H.

Herb production · 1.5 ha · Poltava

Common Questions

Valvix begins generating initial suggestions after 2 full growing cycles. Suggestions become significantly more accurate after 4–6 cycles as the model builds a richer baseline.
No. All suggestions are optional. You can review the supporting evidence, discuss with your agronomist, and decide which to apply. Valvix tracks outcomes regardless.
The system flags suggestions that deviate significantly from standard agronomic ranges and requires additional confirmation before they can be applied.
Valvix optimization works for any crop where you can measure environmental conditions and log yield outcomes. It has been used successfully for tomatoes, peppers, cucumbers, lettuce, herbs, strawberries, and roses.
The system works with approximate yield data. More precise data (by zone and grade) produces more accurate suggestions, but even farm-level totals are useful for the correlation models.

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