Agri & Environment

We work on the application of the Internet of Things technology in agriculture such as precise agriculture.

The use of agricultural control systems for sowing and fertilizing makes it possible to reduce the cost of seeds, fertilizers and fuel, and reduce the time spent on field work.

The use of unmanned aerial vehicles (UAVs) help to reduce the consumption of water and pesticides, reduce labor and resource cost

An important role in agriculture is dedicated to software for enterprise resource planning, it allows you to optimize any process from procurement before production and marketing. The use of such software provides the farm (or an enterprise associated with it) with an opportunity to respond to problems related to environmental protection, adjust the system accordingly, increase the return on investment in their own business;

We bielive that benefits can also be obtained by using technologies such as blockchain (DLT). In particular, the blockchain technology was successfully applied to identify low-quality food products in food supply chains, which allowed timely effective measures to be taken.

The same technology allows the consumer to provide information on the origin of food products, which provides its users with a competitive advantage

The SmartWater

Project started: October 2016

Team Dev:
Yana Krytska, Data Scientist, Ecologist
Artem Velykzhanin, Product Engineer
Inna Skarga-Bandurova, PM

The SmartWater is an interdisciplinary research project focused on development of IoT-based water quality monitoring system and analytic tools.

The technology is primarily targeting the following water based sectors, although not limited by them:

  • Environmental monitoring (open water bodies in ecologically sensitive areas; water reservoirs; rivers, lakes; waste water monitoring, ground water monitoring);
  • Aquaculture (Extensive, semi-intensive aquaculture farms; Land based aquaculture; Controlled environment aquaculture).

Practical Utility:

  • Complex water resources management based on the basin approach, providing continuous assessment of the individual characteristics of each section of the water body and compliance with standards
  • Integrated assessment of the ecological status of the reservoir within each river basin in accordance with the EU Water Framework Directive 2000/60/EC
  • Free access to environmental information

Key components:
The SmartWater system leverages the set of immersion IoT devices equipped with sensors which measure the water quality parameter such as pH, turbidity, conductivity, dissolved oxygen, temperature. Device is designed as a tube (external capsule) and module stack with equipment supporting structure (internal capsule). The external capsule is hermetically sealed.

The basic idea is to place the capsule directly into the water and obtain data using preplanned sensors activation schedule. All instrumentation equipment is placed on the module stack inside the tube.

Assembly blueprint of the SmartWater immersion device


The SmartWater dashboard combines and displays data from various IoT devices spatially and graphically.

The decision-making process for water purification from pollutants includes real-time data monitoring to detect changes in water quality. This requires a deep understanding of the relationship between the quality of water and the need for its adjustment. Similarly, the task of detecting pollution incidents, to support the optimization of cleaning processes, can be used in two ways: the analysis of threshold values ​​and the use of models to perform cleaning processes.

We use statistics and machine learning for

  • real-time surface water pollution risk and impacts analysis
  • contamination models
  • flood models to simulate what would happen if an incident like a water main burst occurs
  • purification process models to determine the necessary steps to regulate the process, for example, chemical dosage of substances to support optimal purification.

The monitoring of the long-term threats to water quality is based on continuous analysis of data over several years to identify trends and ongoing changes in the baseline scenario. The information received from the monitoring system can help in developing strategies to respond to the deterioration of water quality. In the long run, a systematic analysis is performed to determine if the baseline for several parameters has been changed at a specific location where the metering station is located and how the baseline for this parameter has changed in several places.