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Apply satellite farming tools and make your precision agriculture practices easier done than said

EOSDA Data Analytics takes advantage of cutting-edge geospatial data analytics along with AI-powered algorithms to boost businesses globally and address the challenges humanity is facing today.

EOSDA Data Analytics (EOSDA) is a global provider of satellite analytics solutions in agriculture and forestry, capable of creating solutions for 22 industries on request.

Manage your fields easier – monitor remotely, reveal issues on the spot, and act timely with a top-notch software for farming:

  • Variable rate fertilizer/seed application based on agricultural productivity map
  • Near real-time change detection displayed on our Field leaderboard 24/7
  • Timely data-driven decisions thanks to remote problem area detection and precision scouting
  • Effecient planning across all of your fields with an advanced field activity log
  • In-depth field state analytics with agriculture weather forecast, vegetation indices, stages of plant growth among other key factors
  • Access to all the data from agricultural machinery on one screen via user-friendly Data Manager
In a world of neural networks and machine learning, nothing is impossible. Bring your business challenge to us, and we will deliver a special solution to you.
  • Land cover classification
  • Land use classification
  • Crop classification
  • Field boundary detection (contour mask)
  • Harvest dynamics monitoring
  • Yield prediction
  • Cloud mask
  • Soil type classification and much more upon request

Real-world applications of our precision agriculture tech

EOSDA has all the right tools to increase agricultural productivity of any field, regardless of its size and purpose.

Crop Rotation

Make better-informed decisions about crop rotation management and predict yields for every field you have.

Frost Damage Assessment

Apprehend frost damage to plants with our cold stress detection technology.

Crop Rotation

Make better-informed decisions about crop rotation management and predict yields for every field you have.

Plant Wilting Detection

Detect wilting crops at an early stage based on satellite data, NDVI, historical weather data, and send scouts to investigate.

Modified Soil-Adjusted Vegetation Index

Monitor your crops at a very early stage of their development and create variable rate fertiliser application maps based on the MSAVI index.

Normalised Difference RedEdge Index

Detect the oppressed and aging vegetation, identify plant diseases, and optimise the timing of harvest thanks to the NDRE index.

Crop Rotation

Make better-informed decisions about crop rotation management and predict yields for every field you have.

Crop Classification

Identify the type of crop growing on the field using our trained neural network.

Crop Damage Assessment

Keep your crops safe with historical and up-to-date data retrieved from satellite imagery always at hand.

Soil Moisture

Monitor soil moisture levels in your fields and react to drought conditions and waterlogging in a timely manner to keep crops healthy.

Forecasting Tobacco and Cotton Yields with WOFOST

Cotton, often called the country’s “white gold,” has a long tradition of cultivation. Its production peaked during the Soviet period. In 1981, Azerbaijan’s share in the USSR cotton farming reached almost 10%, which amounted to 1.5% of global production.

Following the collapse of the Soviet Union, it dropped (Azerbaijan’s focus on the petroleum industry was a concomitant factor, too.) Searching for promising sources of income after the 2014–2016 global surge in oil prices, the Republic of Azerbaijan resumed its interest in the cotton sector.

Compared to the 2015/2016 market year, the cotton area in 2022/2023 has quadrupled, while yield per hectare increased by about 1.6 times.

Field-scale, regional, and country-wide crop yield prediction is indispensable for agricultural research, farm management, and import and export decision-making.

Challenge: Finding The Optimal Method Of Predicting Yields For Cotton And Tobacco On A District Level.
The project was large-scale and included the classification of several crops, besides cotton and tobacco yield prediction, which we carried out. The forecast had to be made for fields in two districts in Azerbaijan — Shaki and Sabirabad.

Solution: Analysing Statistical, Meteorological, Soil, And Phenology Data With The WOFOST Model

A customer provided statistical information on cotton and tobacco yields at the district level from 2000 until 2017.

The team, in turn, collected the remaining data. It included:

  • Statistics indicating the features of crop cultivation necessary for a model to make predictions or calculations
  • Geographic location of fields and their areas
  • Dates of transplantation of tobacco plants from greenhouses or seedbeds.
  • Meteorological data (humidity, precipitation, wind speed, maximum and minimum temperatures) from the Global Telecommunication System (GTS) and some specific models estimating of evapotranspiration and global radiation.
  • Soil (soil moisture content and rooting depth) data.

In addition, the project team found the optimal sowing/transplantation dates for tobacco and cotton for 2010–2018.

Historical weather data is also indispensable for such tasks. Specialists sourced it from NASA POWER datasets. Meteorological parameters in NASA POWER datasets are taken from NASA’s Modern Era Retro-analysis for Research and Applications (MERRA-2) and the GEOS version 5.12.4 assimilation models. MERRA-2 is a new version of the Goddard Earth Observing System (GEOS) developed at the Global Modeling and Assimilation Office (GMAO) at NASA’s Goddard Space Flight Center.

EOSDA Solutions

EOSDA products and solutions integrate satellite imagery and proprietary AI-powered algorithms to drive business decision-making and endorse sustainable practices.

EOSA Crop Monitoring

Digital satellite-driven precision agriculture platform for cost-effective and sustainable EOSA Crop monitoring and farm management.

EOSDA LandViewer

A catalog of satellite imagery and a tool for processing satellite data.

EOSA Forest Monitoring

Digital satellite-driven platform for cost-effective and sustainable forest management.

Solutions on Request

Satellite-based and AI-powered solutions tailored to specific agricultural problems.

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