Hey everyone, I'm a 19-year-old CSE student from Punjab, India, and I've been building an AgTech project called Satvian mostly on my own.
I started it because I kept seeing agricultural information scattered across different places — weather in one app, mandi prices somewhere else, crop advice somewhere else, satellite data somewhere else — while a farmer often just wants to ask:
“What should I do with my field today?”
So I tried building the whole workflow around the farm itself.
A farmer can ask questions through text or voice in English, Hindi or Punjabi, and Satvian combines things like:
The goal isn't just to return a generic chatbot answer. For example, instead of simply saying “wheat needs irrigation during tillering,” the system can take the farmer's actual field conditions, recent rainfall, forecast, soil information and satellite indicators into account before recommending an action.
I'm also working on the market side. Satvian can compare mandi prices across districts and consider the farmer's location and travel/transport cost, so the question becomes more like “where would I actually make more money selling this crop?” rather than just showing a price table.
I've also built things around it such as:
field polygons → satellite monitoring → crop health history → AI advice → farm memory → mandi intelligence → farmer community
The app is already live on Android, and I've had users from multiple countries testing it even though the main focus right now is India.
I'm currently in the testing/validation stage, and I'm much more interested in finding out where this is actually useful or where I'm completely wrong than adding another 50 features.
I'd especially like feedback from people who work with:
What would make a system like this genuinely useful on a real farm rather than just being another “AI for agriculture” app?
I'm happy to share the technical architecture or explain how any particular part works.
Satvian: https://play.google.com/store/apps/details?id=com.satvian.app
I’ve been working on Meteon, a field-level digital twin for agriculture, and I’m now looking for real farms to test the whole system on.
It combines weather, soil, terrain and satellite data to analyse things like irrigation needs, frost risk, crop variability and field conditions across different zones.
I’m mainly interested in seeing how well the model matches what farmers already know from experience — for example areas that dry out faster, stay wetter, freeze first, or behave differently from the rest of the field.
If anyone is willing to let me test Meteon on one of their fields, I’m happy to provide the full analysis for free.
In return, I’d only ask for honest feedback on what seems accurate, what doesn’t, and what would actually be useful in day-to-day farming.
If you’re interested, feel free to comment or DM me.
Engineers at North Dakota State University have developed and field-tested an autonomous mechanical weeding robot that achieved 96.5% weeding efficacy in sugar beet trials.
The system combines an Amiga robotic platform with a three-row cultivator featuring independently controlled sweep blades for precise, site-specific weed removal.
Using RTK-GPS, strain gauge load cells and laser distance sensors, the robot can adjust blade positioning and depth based on field conditions.
Trials showed that the system could achieve 50% weed coverage while reducing overall soil disturbance by 48.7% compared with less targeted cultivation.
The robot maintained an average tillage depth close to the 30-millimetre target, demonstrating precise mechanical control.
However, its current operating speed of about 0.5 metres per second remains a major limitation for large-scale commercial deployment.
Researchers see faster actuators, improved positioning updates and multiple robots working in parallel as key steps toward commercial-scale autonomous mechanical weeding.
A useful case study from Coggan Farms in Queensland shows why agricultural autonomy probably needs to be evaluated differently from conventional machinery.
During one winter harvest, Tom Coggan spent 18 hours operating the header while two SwarmFarm robots each completed another 18 hours of spraying. That produced 54 operating hours inside one 24-hour period.
It is sometimes summarised as “a week’s work in a day”, but I think the more useful interpretation is parallelisation rather than speed.
The robots were reported operating at roughly 8–9.5 km/h, so this was not about making a sprayer dramatically faster.
Coggan Farms already used camera-based spraying. Their remaining bottleneck was labour: keeping operators in two sprayers for long periods during busy seasons was difficult. Autonomy allowed spraying to continue while staff worked on harvest, maintenance and other farm jobs.
There are important caveats.
The farm had to improve paddock mapping, including boundaries, headlands and obstacles. Changing conditions could require maps to be updated. The robots also still required inspection, servicing and chemical/refill logistics.
That suggests a better autonomy business case should probably measure:
For large broadacre farms with repetitive jobs and labour constraints, that can be a very different equation from a smaller or highly fragmented property.
Coggan Farms was still publicly operating two SwarmFarm units in 2026 and adding automated refill capability, so this appears to have moved beyond an initial trial.
The broader lesson seems fairly practical: agricultural robots do not necessarily need to outperform a human-driven machine on instantaneous speed. They can create value by removing the requirement that every operating machine has a person continuously sitting in it.