Moscow

IntTerra

Unified management system for the agronomic service of agricultural enterprises

AgroTech

farming

SkyScout

Bumbtech

A system designed for automatic monitoring of the state of the bee hive

cloud_service

computer_vision

artificial_intelligence

Bee House

ExactFarming

ExactFarming allows farmers and agronomists to plan agricultural activities and perform them on time, therefore mitigating the negative impact of adverse weather conditions and increasing business profitability./p>

The system provides an electronic map, the fields card, reports based on the results of the fields examination, plan of agricultural works and each field’s expenses, resource management data, cost accounting data, and also information about the weather, vegetation index, crop rotation, satellite imagery, flow process charts, scouting, etc. In addition to the website, there is also <a href="https://play.google.com/store/apps/details?id=com.exactfarming&amp;hl=ru" target=«_blank">mobile app</a>.

ExactFarming supports integration with Caipos, Kataon, Sokol-M, Barani, Meteobot weather stations and ANC soil sensor. The service offers partner producers of sensor solutions to use API Sensor Hub programming interface for integration with data collection systems (sensors). API Sensor Hub allows to display information from sensors directly in ExactFarming service.

<a href="https://app.exactfarming.com/#/auth/register" target=«_blank»>Connect to the system</a>

Online service for agricultural enterprise management

online_service

UrbaniEco

Urbanifarm is created for all-year-round herbs, lettuce and berries production. It provides 1400 kg of lettuce per month in 100 m2 &nbsp;area. The farm includes: pillars for planting, pillar stands with integrated systems for supply and drainage of nutrient solution, lamp stands, wires and controllers, LED lighting, batch plant with computer automatic control and smartphone control, greenhouse, pipelines for solution supply and drainage, climate-control system.&nbsp;

Urbanifarm does not require specialized knowledge in agronomy, soil, herbicides and pesticides, and is installed in rooms with area starting from 50 m2.

UrbaniFarm

Thingenix

The TNGX.IO provides collection, processing and visualization of data from any devices and sensors both by Thingenix or by third parties over the networks LoRaWAN, NB-IoT and LTE-CatM1; controls device configurations and software, incoming data analysis and event notifications.

Due to a large list of sensors (concentration of different gases, temperature, humidity, pressure, vibration, lighting, soil temperature and humidity, weather conditions, etc.), the platform is applicable in industry, farming, municipal and other services.

LPWAN optimized IoT service delivery platform with device and data management

smartcity

housing_and_utilities

agrotech

internet_of_things

LoRaWAN

TNGX.IO

The system that monitors the state of planted acres and the health of plants using drones with special equipment.

The collected measurements and photos are sent to platform developed by the company which returns users structured data and recommendations on how to maintain crops.

The technology allows agriculture businesses to increase crop yield, minimize costs on application of protection production for your plants, reduce the harmful impact of agricultural chemicals on human health, and cut down expenses, the company states.

Field monitoring system enabled by drones

quadcopters

drones

AgroDronGrupp

Cognitive_Pilot

Cognitive_Technologies

The autonomous control system based on artificial intelligence is designed for installation on agricultural machinery – farm tractors, combine harvesters. The automated system, in addition to the AI system, includes a camera and a calculator with additional interface units with combine harvester’s onboard systems.

The Cognitive Agro Pilot is based on the technology of deep learning for neural networks. The Cognitive Technologies’ team of developers in real-life conditions worked on collecting datasets (the images that contain the main elements of a field environment used for neural network training) and taught neural networks to perceive objects and situations a smart combine harvester will confront during the process of its work and which should be recognized by the artificial intelligence.

AI systems can see different types of boundaries of a field (edge, cutting), eliminating the need in installing extra sensors on the equipment. The solution also allows for adding new types of boundaries (rowing – the shape of planting certain crops, for instance, corn, as well as that of tilled and untilled parts of a field).

Tech Solutions

SkyScout

Bee House

ExactFarming

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UrbaniFarm

TNGX.IO

AgroDronGrupp

Cognitive Agro Pilot

Tech Solutions

SkyScout

Bee House

ExactFarming

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UrbaniFarm

TNGX.IO

AgroDronGrupp

Cognitive Agro Pilot

add your project!
fill out the form