In production
Agri Camera
2048×1540 · 12-bit · 80 ha
Compact field camera with built-in multispectral sensor, GPS, and a hyperlocal weather station. Captures RGB and NDVI three times a day. Solar-powered, eSIM, maintenance-free.
Global ground-truth infrastructure
for agricultural AI
Field cameras, calibrated soil sensors, full seasons of data, and ready-to-use backbone models — the physical layer that modern agricultural AI systems are built on.
Problem
Software teams need hardware — and find nothing ready to use.
Research labs building agronomic agents need sensor-fused data grounded in reality. AI companies want to license a ready-made imaging pipeline — not build one from scratch.
The market has split clearly: companies that want to build software, and the infrastructure that should support them. That infrastructure is missing.
Software
Wants to build a product, not deploy sensor networks
Hardware
No ready field infrastructure tuned for ML pipelines
TIME
It takes years to collect historical data covering full seasons
Solution
Ready-made Ground-Truth infrastructure. From field to your model — in one click.
While others stumble over the physical world, we built an end-to-end data platform. We've combined a network of field sensors, automated processing pipelines, and ready ML models into a single ecosystem. All that's left for you is to plug in.
01
Data layer
Sensor-Fused Data
Nothing to deploy. You get a continuous, synchronized stream of real-world data: multispectral plant imagery, soil metrics, and high-precision weather — already structured for ML pipelines.
02
Intelligence layer
Pre-trained Models
No months spent on baseline training or labeling from scratch. We provide core models already trained on our multi-year historical datasets of real field imagery.
03
Distribution layer
Global Scale
We don't just collect data — we help deliver value. Our infrastructure lets software companies seamlessly roll out their products and make AI analytics available to farmers worldwide.
Hardware
Three Devices. One infrastructure.
The Cropler hardware ecosystem consists of three specialized devices. They work in perfect sync to cover all your agro-AI needs — from commercial crop monitoring to deep R&D workflows.
In production
Soil Moisture Sensor
±3% · 10 cm step · IP68
Capacitive soil moisture probe with readings every 10 cm down to 60 cm depth. 3-year battery, BLE transmission, time- and location-synced with the camera.
In development
Research Camera
8MP · Edge AI · ToF · 8× zoom
Next-gen hardware for research workflows. Sony sensor, STM Edge AI chip, ToF for 3D biomass measurement, fisheye + zoom, active cooling for lab-grade precision.
Together, these three devices give you a synchronous top-to-bottom snapshot of the plant: canopy state, spectral anomalies, weather right above the field, and soil moisture in the root zone — all in a single pipeline ready for integration.
Data
Not snapshots. A living history of every field.
Most datasets are slices. That's not enough for models that need to understand growth dynamics, predict stress, or give agronomic recommendations. Cropler collects data continuously across the entire season.
RGB
NDVI
W04 · Tillering
RGB
NDVI
W18 · Flowering
RGB
NDVI
W26 · Maturity
01
NDVI Dynamics
Plant health index captured 3 times a day, building a high-resolution temporal curve.
Your models transition from basic computer vision to advanced time-series forecasting. The AI learns the velocity of growth — tracking how NDVI accelerates during tillering, dips during drought, and recovers after rain.
02
Hyperlocal Weather
Real-time temperature, humidity, and solar radiation measured on-device at the exact moment of image capture.
Eliminates environmental noise. Instead of using delayed, averaged data from stations 20 km away, every image carries its own microclimatic metadata. Your model instantly correlates visual plant stress with immediate weather spikes.
03
Soil Moisture Profile
Volumetric water content (VWC) and soil temperature sampled every 10 cm, down to a depth of 60 cm.
Unlocks predictive modeling. Your AI forecasts final yields weeks before stress becomes visible on cameras, and evaluates fertilizer efficiency by tracking whether nutrients are actively dissolving or sitting stranded in dry topsoil.
04
Regional Personalization
Every data point is natively tied to a specific physical field, regional microclimate, crop hybrid, and agroclimatic zone.
Building highly accurate, localized AI solutions for specific regions. Your models escape inaccurate, averaged global datasets and train on ground-truth data from the exact territories your clients farm in.
Models
A backbone trained on real fields around the world.
Engineered across 28 countries spanning all climate zones, this is the first foundational vision dataset that captures the global reality of agriculture.
Training a good agricultural model is a data problem, not an architecture one. Most teams start with ImageNet and fine-tune on a hundred photos. The result is predictable. We provide a backbone pretrained on thousands of hours of real field imagery across climates and crops.
01
Universal Backbone
A versatile, pre-trained core vision model that acts as an instant foundation for any crop-related ML task.
Plug-and-play integration. Skip months of training generic models from scratch; drop this backbone into your pipeline to get immediate biological intelligence with minimal engineering effort.
02
Segmentation-ready
A specialized spatial model optimized for geometric, high-resolution RGB pixel-level dense prediction.
Precise shape and structure recognition. It isolates tissues from soil and supports an arbitrary number of custom classes—allowing you to map specific plant organs, lesions, or crop-weed boundaries by simply plugging a decoder on top.
03
Feature Extractor
A multimodal network that fuses visual spectrum (RGB) and infrared dynamics (NDVI) into a rich semantic embedding space.
Advanced physiological diagnostics. It catches invisible anomalies and early-stage stress before they manifest visually, delivering clean feature embeddings for downstream classification, yield regression, and anomaly detection.
04
Context for Agents
An API endpoint that fuses three data layers — crop imagery, soil telemetry, and hyperlocal weather — into a single structured text or vector representation.
Powering autonomous agronomic agents. It translates the entire physical reality of the plant into a clean context package. Feed it directly into your LLM so it can understand the complete environment and generate expert advice.
Who it's for
Four audiences. One infrastructure.
Cropler eliminates the friction of physical field deployment, delivering tailor-made data layers and model outputs for every stakeholder in the agricultural ecosystem.
01
Researchers & Labs
A continuous, ground-truth research pipeline with zero operational overhead.
High-fidelity physical data streams synced with multispectral imagery, eliminating the need to engineer, deploy, and maintain your own field sensor networks for R&D.
02
Seed & Fertilizer Producers
Empirical, week-by-week verification of product performance in the real world.
Continuous tracking of exactly how crops respond to your inputs—mapping everything from vegetative canopy speed (NDVI dynamics) to actual root-zone resource consumption.
03
AI Solution Developers
Complete infrastructure abstraction to radically accelerate your time-to-market.
Ready-to-use data streams, pre-trained vision backbones, and multimodal context layers available via API. Focus on building your core software product, not the hardware under it.
04
Agronomy Professionals
24/7 hyper-local situational awareness for high-stakes decision-making.
A unified dashboard aggregating plant health, deep soil telemetry, and canopy-level microclimates. Spot stress factors early, optimize field interventions, and deliver accurate agronomic advice.
Why Cropler
We see the plant every day.
01
Accessible and mobile
A 500 g camera, solar charging, eSIM — install it between rows and forget it. Up to 80 ha per device.
02
Data you can't get anywhere else
Drones — once every two weeks. Satellites — 10 m/pixel. Weather stations — 30 km away. Cropler — three times a day, right above the plant, with soil and weather.
03
Built for AI
Standardized schedule, time and location sync, time-series, pretrained backbone — structured for an ML pipeline.
Contact
Build the product,
not the infrastructure.
Tell us about your use case — we'll match the right level of integration, from dataset licensing to deploying a custom sensor network for a research project.