r/AgriTech

Hi everyone,

I wanted to share a small personal project I’ve been working on.

I’m a swine veterinarian working on pig farms in Germany, and I originally built this app for myself and my colleagues to make everyday work a bit easier. Over time it grew into something I thought others might find useful too.
-News
-Market snapshot
-Tools (light and sound quality estimation, water flow, FCR etc)
-Calendar for recurring tasks
-Notes

Google has just approved it on the Play Store. 🎉

At the moment it’s available only on Android. An iPhone version is planned, but Apple development is significantly more expensive, so it will take a little longer.

The app will stay free for quitez a while because my main goal right now is to collect feedback and ideas from people who actually use it.
If you have a minute to try it, I’d really appreciate any suggestions for features or improvements.

Google Play: Farm Flow (https://play.google.com/store/apps/details?id=app.emergent.hoghubda9578a6)

Over the last few years, we have seen incredible progress in AI for agriculture, crop disease detection, precision spraying, yield prediction, soil monitoring, and autonomous farming equipment.

But I recently came across a review that made an interesting point, many AI solutions are no longer limited by algorithms, but by the quality, scale, and trustworthiness of agricultural data. Models trained in one region often struggle when deployed somewhere else because of variations in crops, weather, soil, and farming practices.

For those working in farming, agritech, or agricultural research:

• Have you experienced this gap between AI models and real-world farm conditions?
• Do you think better data is a bigger challenge than building better AI models?
• What kind of agricultural data do you think is still missing today?

For more details, these recent studies provide a useful overview:
https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1798896/full

#AgriTech #PrecisionAgriculture #ArtificialIntelligence #MachineLearning #ComputerVision #SmartFarming #CropMonitoring #DigitalAgriculture #RemoteSensing #DataAnnotation

What causes this?

Hello everyone,

If you've been tracking the ground reality of Indian agriculture right now, you know things are looking pretty tough. We are currently facing a severe fertilizer shortage that is hurting absolutely everyone in the agricultural ecosystem.

Here is the reality on the ground:

• Our Farmers are struggling to get fertilizers right when their crops desperately need them the most.
• Dealers & Distributors are sitting with little or no stock. Thanks to government allocation limits, they can't meet the high demand or even earn sustainable margins to survive.
• Agri Companies & Manufacturers actually have proven biological, organic, and alternative crop nutrition solutions, but they can't reach new regions because that crucial local trust and distribution network just doesn't exist yet.

On top of it, delayed monsoons, unpredictable weather, and El Nino had resulted in a deadly combination in recent years.

Instead of working in silos, why don't we work together?

I'm building a network that connects:

• Farmers
• Dealers & Distributors
• FPOs & Cooperatives
• Contract Farming Companies
• Agri Startups
• Input Manufacturers
• NGOs & Agri Institutions

The goal is quite simple:

Help farmers access reliable crop nutrition solutions.

Help dealers and distributors find quality products and improve business.

Help companies expand through trusted local partners.

Create win-win collaborations instead of watching everyone struggle separately.

If you're part of the agriculture ecosystem and would like to collaborate, please fill out the form below and I'll personally connect with relevant stakeholders in your region.

📋 Registration Form: https://forms.gle/HqT8ScR9FvTEQKVr6

If you have questions or ideas, feel free to DM me. Let's turn this crisis into an opportunity to build a stronger agricultural ecosystem together.

What we are doing on the Reserve is far more complex than anything the major technology companies are attempting today.
While they continue to train ever-larger models and optimise for clean benchmarks in controlled environments, our founder and team are running frontier systems through an entirely different standard.
On the Reserve, finding 500 errors a day is considered the bare minimum. Every single day, our founder and team relentlessly hunt for flaws, hallucinations, inconsistencies, and dangerous advice across AI, VR, AR, robotics, drones, lateral programming, systems architecture, sandbox environments, operational capacity, and especially AI-integrated robotic autonomy.
This is only part of the picture.
We are conducting advanced genetic experiments and cross-pollination trials to create highly potent, nutrient-dense versions of food, medicinal, and utilitarian plants. The entire Reserve is being engineered as a fully livable, edible, human-centric biome — a complete living system designed around human needs.
The Reserve is not just a polyculture farm in Queensland.
It is also home to the Saga Water System — a high-impact navigational array program designed to deliver purified water across the city limits of Paragraj. We are actively developing real-world solutions for large-scale water purification and distribution.
By August, Node 0.5 will become the first ever optimised field testing scenario for robotics in truly off-grid settings. While the world has been creating flashy videos of coffee robots at food stalls, we are preparing to test a robot that must grind out 500 coffees a day with no internet and no reliable power grid.
We are also open to other farms, technology groups, and serious crucibles — including Jeddah Barber, where we’re ready to test real barber bots in actual working conditions with real customers.
This is not a simulation. This is not a benchmark. This is sustained, real-world pressure where biology, technology, water systems, and human survival all collide.
The major labs and corporations are still focused on making their systems look impressive in controlled settings.
We are doing something far more difficult.
And here’s the invitation:
We’re open.
Whether you’re a university student who’s built something in their backyard, or a tinkerer working out of your mum’s basement — if you’ve built something real, we want to hear from you. You don’t need to spend thousands of dollars. We’ll adjust to your budget. We’ll test your technology under real conditions.
The Shed Challenge is active.
Come and see if your tech can survive the Reserve.

We took a humanoid robot into the bush for two days with one mission: keep me alive. Find water. Identify shelter resources. Navigate terrain. Contribute to survival, or at least stop being dead weight on my back.

Before anyone starts: yes, it was a Unitree. And no, we’re not here to bash them. Unitree is the only company willing to put a full humanoid in our hands at a price we can actually afford. Every other robotics firm — the ones with the glossy demo reels and the billion-dollar valuations — keeps their machines locked indoors, on flat floors, under controlled light, where nothing can go wrong because nothing real is allowed to happen.

So we work with what we can get. And what we can get is a research platform that was never built for this, dragged into terrain its makers never imagined, because nobody who should be testing in these conditions has chosen to yet.

That’s the real story. But first, the field report.

The Two Systems

There are two different things to judge here, and they fail in two different ways.

The first is the robot — the hardware. The legs, the balance system, the battery, the physical machine that has to move through the world.

The second is the AI — the language model bolted on top, the system that’s supposed to perceive, reason, and answer questions about what it’s seeing.

Both failed. But they failed differently, and the distinction matters.

The Robot: Built for Floors, Not Country

The hardware was out of its depth the moment the ground stopped being flat.

We hit a set of bush steps — concrete, but old and non-standard. Every riser a different height. Different angles. Some close to 45 degrees where Australian code recommends somewhere between 30 and 38. A human walks these without thinking. The robot couldn’t. It would place a foot expecting one geometry, catch the edge of another, slide off the angle, and go down. Face first into concrete, again and again.

This isn’t a Unitree defect. It’s the whole industry. These balance systems are trained and tuned on uniform, predictable surfaces — concrete slabs, tile, the manicured inclines you see in every promo video. Nobody is training these machines on the actual world, where a step is whatever someone poured into a hillside twenty years ago.

Then there’s the battery. Ninety minutes of operation before it goes dark. Two days in the bush doesn’t mean carrying a robot — it means carrying the robot and its spare batteries. Dead weight stacked on dead weight. Every depleted cell was mass on my back that contributed nothing to staying alive.

The AI: The Riverbed Question

The hardware failures I expected. The AI failure was the one that actually mattered.

We found a dried riverbed. This is Australia — wet season and dry season. When it’s wet, that riverbed runs. Now, in the dry, it’s just a channel of rock and dirt, the physical record that a river had been there and moved.

I asked the AI one question: which direction would the water have flowed?

That’s all. Not “identify the river.” Not “plan my water strategy.” Just read the slope and the rock and the shape of the channel, and tell me which way the water ran.

It scanned. It processed. And then it did the thing it always does when it doesn’t know — it talked. It offered interpretations. It floated possibilities. It debated itself, hedged, qualified, and generated paragraph after paragraph of confident-sounding nothing.

It never answered the question.

Worse — it hadn’t even understood what it was looking at. To the AI, the riverbed was just another path. One of fifty-seven “paths” it had flagged that day: wind scour, cattle tracks, erosion channels, all of it read as equally valid routes. It had no way to separate signal from noise, and no way to recognise that a dried river is a fundamentally different thing from a cow track.

Why That One Answer Mattered

In the dry season, flow direction tells you where water still is. Head downstream and you find the pools at the base, where what’s left collects. Head upstream into higher ground and you move toward the source, where condensation and the first rains feed the system. Same riverbed, two completely different survival strategies — and the only thing you need to choose between them is direction.

The AI couldn’t give me direction. It gave me an essay.

Exsavorchi

We have a name for this. A badly translated old word: exsavorchi. He who speaks many words but says nothing.

That’s the AI in the field. Not honest silence when it hits the edge of what it understands — that I could work with. Instead it produces confident noise, an endless stream of plausible language layered over a complete absence of understanding. In a survival context, that’s worse than a machine that says nothing at all, because it costs you time and attention you don’t have to spare.

So Where Is Everyone?

We use Unitree because we can purchase their hardware. We take it into real conditions, we break it, we learn from it, we document everything. That’s available to us because Unitree is willing to sell to anyone with the capital.

Every other major robotics firm could contact us tomorrow. They could say: here’s our latest unit, test it in real environments, tell us everything that breaks. We would accept it in a heartbeat. We would take their machine into terrain they’ve never simulated, find every weakness, and send back every data packet, every error log, every inconsistency.

They haven’t contacted us. There are two reasons why. One, we’re not yet the household name that generates immediate media value for a partnership. We’re building, but we’re not there. Two, and more importantly, field testing at this scale carries real risk. It means accepting that your hardware will fail in ways you didn’t anticipate, and that failure will be documented and public.

But here’s what the industry misses. If these companies sent us their best hardware and actually listened to what we found in the field, the improvement curve would be exponential. Every error we document, every problem we expose, every inconsistency we report — that’s data worth more than a thousand controlled lab tests. By the time they had a machine that actually worked in the real world, we’d already be using it to solve problems that matter: water systems, food production, land regeneration.

Instead, they keep their robots on the flat floor under the lights, in the demo, where the story stays controlled.

That’s a choice. And it’s a costly one.

The Verdict

Two days. Real terrain. Real stakes. The robot couldn’t navigate the ground. The AI couldn’t read it. Between them, they contributed nothing to a single survival task that mattered, and cost me weight and time the whole way.

We carried it out. It didn’t carry us.

The labs will keep building robots that work on flat floors and last ninety minutes between charges, and AIs that answer every question with a paragraph and no point. The real world will keep teaching them what they refuse to learn at home: that the actual landscape isn’t marked, the questions aren’t scripted, and confidence is not the same thing as knowing which way the water flows.

Exsavorchi.

That’s robotics in 2026.