Hey everyone — Wonka here, Product Manager for YUNO.

There was one question we kept coming back to while designing YUNO:

If the feeder dispensed food, does that actually mean your cat ate?

Not necessarily.

Most automatic feeders can confirm that food was released.

That’s useful — but when I’m away from home, the questions I actually care about are a little different:

Did my cat actually come over to eat?

Is there still food left in the bowl?

Does the next scheduled feeding really need to happen?

That difference became an important part of how we approached YUNO.

Feeding isn’t just one event

A typical automatic feeder can think about feeding like this:

Scheduled time → dispense food → done.

https://preview.redd.it/le9g5m6r4qnh1.png?width=2396&format=png&auto=webp&s=47dc92cea0d3d7158378c5b8b2a0f41797d71a34

But real feeding is more complicated.

A cat might come over immediately.

They might eat only part of the meal.

They might walk away and come back later.

Or there may still be plenty of food left when the next scheduled feeding arrives.

So for YUNO, we wanted to understand more of what happens around the meal:

Food dispensed → cat approaches → eating activity → food remains in the bowl → next feeding

That’s why the camera became more than just a way to check in on your pet.

YUNO is designed to recognize when food is dispensed and detect when a cat approaches the bowl and starts eating.

That helps answer two different questions:

Did the machine work?

and

Did my cat actually interact with the food?

Those are not the same thing.

Then we ran into another problem: leftovers

Let’s say the next scheduled feeding arrives, but there’s still a significant amount of kibble sitting in the bowl.

If the feeder simply keeps dispensing according to the schedule, fresh food can keep piling on top of older food.

Over time, that food stays exposed to air longer, which can affect freshness, texture, and crunch.

So we started working on another question:

Can YUNO understand how much food is still left in the bowl?

We developed a leftover-food detection model that classifies the bowl into several states:

• Full
• Half
• Low
• Empty

That sounds fairly straightforward until you start testing it in real homes.

Real bowls are messy

A clean bowl under perfect lighting is easy.

Real life isn’t.

People use different bowls.

Kibble comes in different colors, sizes, shapes, and textures.

Lighting changes throughout the day.

Bowls collect crumbs, scratches, residue, and marks from long-term use.

So we trained and tested the leftover-food detection system across conditions including:

• different bowl shapes, colors, and materials;
• different kibble colors, sizes, and textures;
• bright daylight;
• dim indoor lighting;
• night-vision conditions;
• clean bowls;
• bowls with crumbs and residue.

The model is based on MobileNet, and in our internal test dataset, it achieved over 99% accuracy in recognizing leftover-food levels across the bowl, kibble, and lighting conditions we tested.

I want to emphasize internal test dataset here.

That number does not mean YUNO will be perfect in every bowl, every home, and every possible lighting condition.

Real environments always create edge cases.

For me, the more important point is what we were trying to solve in the first place.

A smart feeder shouldn’t stop at:

“Food was dispensed.”

We want it to help answer:

“Did my pet come to eat?”

“How much food is still there?”

“Is another feeding actually needed?”

Because feeding isn’t really about whether a motor turned at the scheduled time.

It’s about what happened before and after the food came out.

I’m curious about this from people already using automatic feeders:

What do you find yourself checking most often?

1. Whether food was dispensed
2. Whether your cat actually ate
3. How much food is left in the bowl
4. Something else entirely

Would love to hear what actually matters in day-to-day use.

— Wonka
Product Manager, YUNO