Arvanos precision agriculture and soil mapping
A SOIL MAP IS THE FOUNDATION OF PRECISION AGRICULTURE
PRECISION AGRICULTURE BEGINS WITH A SOIL MAP.
Agriculture is entering the era of precision management.
Satellites, drones, autosteer systems, robotics and AI already make it possible to manage fields more precisely, respond faster and reduce manual decision-making.
Yet all of that precision depends on one fundamental layer: a detailed soil map.
A SOIL MAP IS THE FOUNDATION OF PRECISION AGRICULTURE
PRECISION AGRICULTURE BEGINS WITH A SOIL MAP.
Analytical results are georeferenced and converted into digital soil property layers: pH, nutrient status, soil organic matter, moisture, compaction and other attributes.
Onboard controllers use these maps for variable-rate application of fertilisers and soil amendments, as well as site-specific seeding rates and tillage depth.
Without a detailed map, machinery can position and meter inputs with high precision, but it does not know which action is required at a particular location in the field.
A SOIL MAP IS THE FOUNDATION OF PRECISION AGRICULTURE
A DETAILED SOIL MAP REQUIRES MANY SAMPLES, TIME AND LABORATORY ANALYSES.
Conventional soil mapping divides the field into a regular grid. One or more samples are collected from each grid cell, georeferenced, labelled, transported to a laboratory and analysed.
The greater the required spatial resolution, the more sampling locations, field routes, samples and analyses are needed. Cost and turnaround time rise with map detail.
A SOIL MAP IS THE FOUNDATION OF PRECISION AGRICULTURE
MODERN METHODS SOLVE DIFFERENT PROBLEMS. NONE PROVIDES THE COMPLETE PICTURE.
Hyperspectral imaging rapidly reveals spatial variability across exposed soil without a full sampling grid.
But a camera cannot observe the subsurface, while moisture, shadows, stubble and crop residue confound spectral reflectance — especially under no-till. The method can delineate areas for ground verification, but it cannot produce an accurate soil property map on its own.
A SOIL MAP IS THE FOUNDATION OF PRECISION AGRICULTURE
MODERN METHODS SOLVE DIFFERENT PROBLEMS. NONE PROVIDES THE COMPLETE PICTURE.
Proximal measurements of apparent electrical conductivity, moisture, penetration resistance and compaction provide information about soil conditions below the surface.
Yet these measurements still describe only part of the soil system. A reliable map requires them to be integrated with remote observations, field history and laboratory analyses.
A SOIL MAP IS THE FOUNDATION OF PRECISION AGRICULTURE
GROWERS NEED MORE THAN A STATIC MAP. THEY NEED A FIELD MODEL THAT KEEPS IMPROVING.
Conventional surveys depend on field access, ground machinery, manual sample collection and laboratory logistics. Results may arrive after the optimal agronomic decision window has already closed.
Growers need to be able to survey a field at almost any stage of the season — even before ground machinery can enter in spring or after the crop has been planted.
A SOIL MAP IS THE FOUNDATION OF PRECISION AGRICULTURE
GROWERS NEED MORE THAN A STATIC MAP. THEY NEED A FIELD MODEL THAT KEEPS IMPROVING.
During crop growth, plant condition reveals uneven development, possible nutrient deficiencies or other stress, and the areas that require further investigation.
The system must detect these zones quickly, determine where ground verification is needed, direct soil sampling and return results while crop nutrition can still be adjusted.
A SOIL MAP IS THE FOUNDATION OF PRECISION AGRICULTURE
A HIGH-ACCURACY MAP IS NOT A ONE-OFF SURVEY. IT IS A CONTINUOUS REFINEMENT PROCESS.
A detailed map is needed continuously, but repeating a complete survey on a regular basis is too slow and expensive.
A next-generation system must use accumulated evidence and acquire only what the map is missing. Each refinement then requires fewer samples, less time and lower cost, making soil mapping more affordable while keeping the map current throughout the season.
This is essential infrastructure for the transition from automated machinery to autonomous farming.
ARVANOS IS AN AI-POWERED HARDWARE-SOFTWARE SYSTEM FOR BUILDING, CALIBRATING AND CONTINUOUSLY REFINING SOIL PROPERTY MAPS
ARVANOS
ARVANOS COMBINES AN AI ORCHESTRATION LAYER WITH DYNAS FIELD AGENTS — AUTONOMOUS AERIAL AND GROUND PLATFORMS EQUIPPED WITH SENSORS AND ONBOARD COMPUTE.
The AI layer analyses data and coordinates the system. Dynas agents observe the field from above, perform subsurface measurements and collect samples for laboratory calibration.
Every component operates as part of one integrated hardware-software system and continuously feeds new evidence back into the AI layer.
ARVANOS
ARVANOS BUILDS THE INITIAL FIELD MAP.
The system integrates field history, historical NDVI, yield data, previous laboratory analyses and other available sources.
It uses this evidence to build an initial map and determine where the data is already sufficient and which areas require additional investigation.
ARVANOS
DYNAS MAYA SEES THE FIELD FROM ABOVE.
Maya is a lightweight aerial field agent in the Dynas line. It surveys the field, acquires current hyperspectral imagery and detects surface variability.
The AI layer analyses the data in real time and, when needed, retasks Maya to survey selected areas at higher spatial resolution. Maya can repeat surveys throughout the growing season, detecting new anomalies and areas that require refinement. In the future, Maya will also enable early detection of crop diseases and pests.
ARVANOS
THE AI LAYER SELECTS WHERE SUBSURFACE VERIFICATION IS NEEDED.
Maya refines the surface view of the field. The AI layer compares these observations with historical layers and selects the most informative locations for subsurface measurements.
These locations are automatically converted into a field task. The agent receives coordinates and a route to the selected points without blanket traffic by ground machinery across the field.
ARVANOS
DYNAS HERUS EXAMINES THE SOIL BELOW THE SURFACE.
Herus is the heavy ground field agent in the Dynas line. It drills, measures moisture, apparent electrical conductivity, penetration resistance and compaction, determines the depth of the plough pan, and performs spectral analysis of soil extracted from the borehole.
ARVANOS
DYNAS HERUS EXAMINES THE SOIL BELOW THE SURFACE.
After analysing the field measurements collected by Herus, the system identifies the minimum set of sampling locations required for laboratory calibration.
Herus collects the samples and returns them to base for labelling and transfer to the laboratory.
ARVANOS
LABORATORY RESULTS CALIBRATE THE MAP.
Arvanos correlates laboratory results with remote and proximal measurements. This ties indirect signals to quantitative reference values and turns the map into a calibrated field model.
ARVANOS
FEWER UNNECESSARY SAMPLES. LOWER COST. FASTER DECISIONS. A CURRENT MAP THROUGHOUT THE SEASON.
Arvanos directs laboratory analysis only to locations where it adds new information to the map, integrating remote and proximal surveys into one managed cycle. This reduces unnecessary samples, field routes and repeat surveys, substantially lowers mapping cost, and delivers results while there is still time to adjust an agronomic decision.
ARVANOS
EVERY STAGE IS COORDINATED IN REAL TIME BY THE ARVANOS AI ORCHESTRATION LAYER.
Every new data layer returns to the system. Arvanos updates the map, shows where confidence is already high and where further verification is needed, and adjusts the next tasks for Maya and Herus.
The initial map progressively becomes validated and calibrated — and continues to improve throughout the season.
Maya sees. Herus does. And Arvanos knows.