🌱Agriculture feeds the world, but most farmers still operate without data, access, or proof. That gap is where $DMTR operates.
@dimitratech combines AI, satellite imagery, and blockchain to give smallholder farmers practical tools that improve yields, prove sustainability, and unlock markets. This is applied infrastructure, not theory.
The timing matters. The #AgTech sector is projected to reach roughly $82 billion by 2035, and Dimitra is positioned directly in the flow of regulation, compliance, and global trade.
AI-driven agronomic advice and satellite monitoring help farmers make better decisions in real time. Blockchain traceability turns those actions into verifiable records that buyers, governments, and regulators can trust.
✅Dimitra is already a leader in #EUDR compliance. It provides deforestation-free proof for coffee and cocoa supply chains, enabling access to EU markets. Honduras deployments are live, and Indonesia onboarding is bringing millions of farmers into compliant traceability systems.
✅The platform operates across more than 65 countries and is accelerating through integration with @MANTRA_Chain to support real-world assets and carbon credit programs.
✅DMTR has clear utility across the system. It is used for payments, staking, revenue-linked buybacks, and real-world asset tokenization tied to agricultural output and environmental data.
Adoption is measurable. Deployments are active. Markets are opening.
DMTR sits at the intersection of agriculture, regulation, and data infrastructure where growth is structural, not speculative.
$ROVR/ @ROVR_Network At first glance, this looks like modern art. A banana, maybe. Minimalist. Abstract.
In reality, this is how machines learn to see the world.
🔹What you’re looking at is a point-cloud world model created from data captured by the ROVR Network’s LightCone. In just a 30-second drive, millions of data points are collected and transformed into a 3D representation of the environment.
🔹Each point holds information about depth, structure, and position. Together, they form a living map. Roads reveal their surface and slope. Trees show height and density. Buildings take shape. Even small changes in terrain become visible. This is not a static image. It’s a snapshot in time of how the real world actually looks to AI systems.
🔹Autonomous vehicles, robots, and spatial AI don’t rely on photos or flat maps. They learn through point clouds like this, where geometry matters more than color and distance matters more than labels. A road isn’t “a road.” It’s a surface with depth, edges, obstacles, and motion around it.
ROVR Network turns everyday driving into this kind of intelligence. Short drives produce dense, high-fidelity world models that machines can train on immediately.
Yes, the color, angle, and orientation were adjusted for creative effect. But the data itself is real.
⚡What looks like art to humans is usable reality for machines.
$ROVR -Autonomous vehicles don’t fail because #AI isn’t smart enough.
They fail because the data is old.
👉Most AV systems are trained on static maps and historical datasets. But the real world doesn’t stand still. Roads are rerouted. New construction appears overnight. Lane markings fade. Weather changes surfaces. Human driving behavior evolves faster than any model update.
Training AI on yesterday’s world creates blind spots.
🌐 @ROVR_Network exists to fix this problem by turning the physical world into continuously updated ground-truth data. Instead of relying on occasional surveys, ROVR collects live 3D and 4D spatial data at scale, directly from roads as they are used today.
Drivers map streets using ROVR hardware, generating high-fidelity data with centimeter-level accuracy. That data feeds world models used by autonomous vehicles, robotics systems, and spatial AI. When roads change, the data changes with them.
Think of it like this:
Static maps are photographs. ROVR data is live video.
🔹Over 35 million kilometers have already been mapped across diverse geographies, giving AI systems exposure to real-world variability instead of ideal conditions. Construction zones, detours, weather impacts, and edge cases are captured as they happen, not months later.
🔹Better data means fewer assumptions. Fewer assumptions mean safer autonomy.
🔹The future of self-driving isn’t just smarter algorithms or larger models. Those already exist. The real advantage comes from training machines on the world as it actually looks, moves, and behaves right now.
Autonomy improves when data keeps up with reality.
🇺🇬Coffee is one of the most regulated agricultural commodities in the world. And Uganda sits at the center of it.
Uganda is Africa’s number one coffee exporter and the fifth largest globally. That scale is why @dimitratech being selected as Uganda’s National EUDR Traceability Platform Provider matters far beyond one country.
EUDR compliance at this level sets a reference point for global supply chains. When traceability works in Uganda, it works anywhere.
Dimitra provides end-to-end traceability using satellite imagery, AI, and blockchain to track coffee from farm to export. Every plot, harvest, and transaction is recorded as verifiable data that can be reused for EUDR, ESG reporting, and carbon accountability.
This is not a pilot. It is national infrastructure.
✅In 2025, Dimitra proved adoption at scale by working with cooperatives, traders, exporters, and government stakeholders. In 2026, the focus shifts to deeper integration.
✅Traceability tools are expanding from cooperatives to exporters, embedding compliance where market access decisions are made. Farmer and aggregator engagement is being strengthened to ensure data quality and long-term usage. Compliance outputs are designed to unlock international markets and durable commercial partnerships.
✅DMTR powers access and operation across this system, linking verified agricultural data to real economic activity.
From Uganda’s coffee fields to global buyers, Dimitra is turning regulation into infrastructure and compliance into opportunity.