Cameras and AI to improve process automation at an agricultural plant

Using cameras and image recognition to take away tedious human monitoring, freeing-up staff for higher value tasks.

Return on investment

40Operator hours/week freedPreviously spent on manual monitoring of the valves.

4Hours of downtime avoided/weekFaster, more consistent auger corrections keep the line running.

98%Classification accuracyReached in production, matching the standard an operator would apply by eye.

Expansion now ready

The removal of the limiting factor on operation allows the whole plant to scale.

Background

A complex, 24/7 production process

Aumkar Plantations in Malaysia operate a 24/7 pyrolysis process, taking agricultural residues and converting them into carbon removals, biochar and green energy. The operation is large and requires multiple pieces of equipment to work in unison to optimise outputs. Previously, level sensors were relied upon to monitor throughputs, but reactions to issues were too slow, resulting in interruptions to production.

The challenge

Keeping every valve filled without a human watching it

Keeping each rotary valve correctly filled is critical to smooth, continuous operation. Too little feedstock underfills the valve, too much overfills it, and either state requires a fill-time correction on the augers. Historically, this has relied entirely on manual visual inspection: an operator watches a live camera feed above each valve, classifies what they see into one of six fill states every 90 seconds, and manually adjusts auger fill time in small ±2 second increments.

One person, two feeds, around the clock

The manual process ties up one operator continuously monitoring two camera feeds, on a line that never stops.

Inconsistent, fatigued judgement calls

Classifying fill state by eye, every 90 seconds, introduces the possibility of inconsistent or fatigued judgement calls.

A ceiling on how far production could scale

Relying on manual monitoring slows Aumkar's ability to scale the operation to more equipment and more sites.

Mainspring solution

A computer vision fill-state classifier

[One or two lines introducing the tool — to be completed]

Reading fill state from the existing cameraMainspring built a computer vision classification model to automatically read the fill state of each rotary valve from the existing overhead camera feed, replacing the manual visual check.
The same six categories an operator usesThe model classifies each image into the same six categories an operator would use: Empty, Underfilled, Filled, Almost full, Overfilled, or Unreadable.
Straight into the PLCThat classification feeds directly into the PLC, triggering the ±2 second auger adjustment automatically.

01 [Feature one][One line on what this screen does] 02 [Feature two][One line on what this screen does] 03 [Feature three][One line on what this screen does] 04 [Feature four][One line on what this screen does]

The outcome

From manual checks to automated, real-time classification

Following full deployment across both rotary valves and integration with the PLC, the manual visual monitoring process has been replaced with automated, real-time fill-state classification driving auger adjustments directly.

Classification accuracy in production has reached 98%.

Operators are no longer required to continuously monitor the valves and sensors; their attention has been freed up for higher-value tasks.

In the first 12 weeks of operations, just 2 unplanned interventions were required.

Lighting variability across the 24/7 operating cycle has been addressed via supplementary LED lighting, with no measurable drop in accuracy across day/night conditions.

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Tom Welch · Solution Engineer