DREVO AI Architecture – An Intelligent System for Ecosystem Analysis and Decision Making

Next-generation artificial intelligence for managing living ecosystems

WOOD AI— is an intelligent decision support system that combines ecology, agronomy, hydrology, forestry, climatology, robotics, and modern data analysis methods.

Its task is not to replace a specialist, but to combine a huge amount of information that a person is not able to process simultaneously.

The main principle of DREVO:

Artificial intelligence doesn't make decisions for humans. It helps us see connections, predict consequences, and choose the most sustainable paths for ecosystem development.

1. General architecture

DREVO AI consists of several interconnected levels.

Data sources → Processing → Analytical models → Forecasting → Recommendations → Management → Feedback → Self-learning.

Each level can develop independently and be connected as the project grows.

2. Data sources

The system combines information from multiple sources:

Field observations

specialist examinations;

mobile application;

photographs;

measurements.

IoT sensors

soil moisture;

temperature;

pH;

electrical conductivity;

water level;

wind;

solar radiation.

TREE AeroSense

multispectral imaging;

thermal imaging maps;

lidar;

electromagnetic measurements;

aerosol analysis;

acoustic analysis of the environment.

DREVO robotic platforms

ground inspections;

soil analysis;

automatic sampling;

landing control.

Satellite data

vegetation condition;

surface temperature;

precipitation;

cloudiness;

long-term climate trends.

Laboratory data

soil;

water;

leaves;

seeds;

microbiology;

chemical composition.

3. Unified information space

All data is collected into a single digital repository.

Each object is associated with:

coordinates;

time;

source;

level of reliability;

digital plant passport;

plot;

guild;

plant community.

This allows us to analyze events not in isolation, but as elements of a single system.

4. DREVO Knowledge Graph

At the heart of artificial intelligence is the knowledge graph.

It connects together:

plants;

animals;

microorganisms;

soils;

climate;

water;

diseases;

technologies;

equipment;

people;

projects.

For example:

tree → guild → soil → moisture → mycorrhiza → crop → insects → birds → biodiversity.

This approach allows us to search for hidden relationships between processes.

5. Analytical core

The DREVO AI core consists of specialized intelligent modules.

Eco AI

Evaluates:

ecosystem sustainability;

biodiversity;

succession;

environmental risks.

Plant AI

Analyzes:

plant health;

height;

productivity;

nutritional deficiency;

stress;

compatibility of species.

Water AI

Works with:

water balance;

humidity;

watering;

water accumulation;

evaporation;

forecast of water shortages.

Soil AI

Analyzes:

soil structure;

organic matter;

compaction;

microbiological activity;

erosion;

degradation.

Climate AI

Evaluates:

local climate;

extreme events;

long-term changes;

climate impact on a project.

Biodiversity AI

Monitors:

birds;

insects;

mushrooms;

wild animals;

natural connections.

Agro AI

Used for:

productivity;

processing;

economic efficiency;

seasonal planning.

Restoration AI

Specializes in the restoration of degraded areas.

6. Spatial intelligence

DREVO AI works with spatial data.

The system understands:

distances;

slopes;

watersheds;

wind direction;

movement of sunlight;

plant distribution;

spread of diseases.

This allows us to analyze not just individual points, but the entire territory.

7. Temporal intelligence

Each event has a time reference.

AI analyzes:

seasonal cycles;

long-term changes;

extreme years;

recovery speed;

growth rate;

climate change.

The history of the project becomes one of the most important sources of knowledge.

8. Reliability assessment system

Each entry is assigned a trust level.

For example:

Very tall— laboratory confirmed.

High— confirmed by several independent sources.

Average- observation by a specialist.

Short- automatic AI guess.

Unknown- not enough data.

This helps to separate proven facts from hypotheses.

9. Digital twin of the ecosystem

AI creates a digital model of the territory.

It includes:

relief;

water;

soils;

plants;

animals;

climate;

infrastructure.

This model can be used to test various scenarios without interfering with real nature.

10. Forecasting

The system is capable of simulating:

tree growth;

change of crowns;

development of guilds;

accumulation of organic matter;

change in water regime;

productivity;

spread of disease;

consequences of droughts;

soil restoration.

All forecasts are accompanied by an indication of the level of uncertainty.

11. Scenario modeling

The user may ask the question:

"What happens if you increase the planting density?"

"How will the project change if the amount of water is reduced by 20%?"

DREVO AI builds several scenarios:

optimistic;

base;

conservative;

stressful.

This helps compare the possible consequences of different decisions.

12. Recommendation system

Based on the analysis, the AI can recommend:

watering;

mulching;

planting;

thinning;

change of guild;

wind protection;

adjustment of water regime;

additional research.

Each recommendation is accompanied by an explanation of the reasons.

13. Explainable Artificial Intelligence

Every decision must answer the questions:

Why did the system come to this conclusion?

What data was used?

What factors were the most significant?

How high is the confidence?

This allows the specialist to verify the findings.

14. Self-study

After each season, the system compares:

forecast;

actual result.

If the forecast turns out to be inaccurate, the model is adjusted.

In this way, knowledge is gradually adapted to the characteristics of a particular region.

15. Integration with DREVO AeroSense

AI automatically analyzes drone data:

spectral maps;

heat maps;

lidar;

crown condition;

plant stress;

humidity.

Suspicious areas are automatically highlighted on the map.

16. Integration with DREVO robots

Ground robots can receive tasks:

measure humidity;

take a soil sample;

take a picture of a tree;

check the condition of the landings;

perform spot watering;

remove weeds.

AI distributes tasks based on priorities.

17. Risk management

The system evaluates the probability:

droughts;

fire;

diseases;

pest infestations;

windfalls;

erosion;

flooding.

Each risk is accompanied by recommendations for reducing its consequences.

18. Collective intelligence

Knowledge is not formed only by algorithms.

The following information can be entered into the system:

farmers;

foresters;

scientists;

volunteers;

local residents.

Once verified, this data becomes part of the general knowledge base.

19. International knowledge network

Anonymized project data can be used for comparison:

climatic zones;

varieties;

recovery methods;

water technologies;

methods of forming forest gardens.

This allows us to accelerate the development of environmental projects around the world.

20. Decision-making architecture

Each decision goes through several stages:

Data collection.

Quality control.

Analysis of relationships.

Building scenarios.

Risk assessment.

Formation of recommendations.

Expert's decision.

Execution.

Monitoring the result.

System training.

This cycle ensures continuous improvement of the models.

21. DREVO AI Ethical Principles

The system is based on the following principles:

priority of nature conservation;

transparency of algorithms;

verifiability of recommendations;

respect for local knowledge;

protection of personal data;

biodiversity conservation;

support for a person, not his replacement.

22. Architecture of the Future

In the long term, DREVO AI will be able to combine:

millions of plants;

thousands of projects;

dozens of countries;

satellite data;

drones;

robotic platforms;

climate models;

scientific research;

traditional knowledge of local communities.

This will make it possible to create a global network for monitoring the restoration of natural ecosystems.

The final DREVO AI model

Data sources → DREVO AI Cloud → Knowledge Graph → Analytical modules (Eco AI, Plant AI, Soil AI, Water AI, Climate AI, Biodiversity AI, Agro AI, Restoration AI) → Digital Twin of the Ecosystem → Forecasting and scenario modeling → Recommendations for a specialist → Robots, drones, and control systems → Monitoring results → Self-learning system.

The final principle

DREVO AI views the ecosystem as a single living organism. Its goal is not to maximize yield at any cost, but to maintain a long-term balance between water, soil, plants, animals, and humans. Only such a system can ensure sustainable restoration of nature, food security, and the preservation of our natural heritage for future generations.