Agricultural Data Science: Harvesting Data

Every day a huge amount of invisible data is streamed over mobile networks. Data can be of different types depending on the source and type, which is why all types of industries rely so heavily on data.

Data science is a multidisciplinary field, combining many subjects such as mathematics, statistics, computer science and business management. It combines various tools and techniques together, which are made for analytical purposes only. From data collection to machine learning and presentation of results to management, each step consists of finding meaningful insights from the given data. Data is used as raw material to find solutions to business problems and predictive analysis of future problems.

One of the major public sectors benefiting from data science in agriculture. Although still in its infancy, it has great scopes and applications.


The farming scene is deteriorating every year with:

  • Poor yielding seeds.
  • Natural Disasters
  • Lack of water and farm equipment.
  • Lack of financial support.

All this leads to under- or over-production for which farmers do not get a fair price and leads to farmer suicides and farms becoming infertile. The problem is that technological innovations and resources are not used optimally.

Several analysis techniques can help improve farmers and their farming practices, such as:

  • Big data
  • Machine learning
  • The Internet of Things
  • Cloud Computing

For all of these tools to work, you need historical and current data to work on. And all this data can be collected from a variety of sources, such as government datasets or from sensors located near farms and machines. Some rich data sources are:

  • Satellite base field imaging
  • GPS sensor based tractors and ploughers
  • Climate and weather forecasts
  • Fertilizer requirement data
  • Information on pests and weeds
  • Sensor-based data from the farms

Analysis of this data can be useful not only to farmers, but also to insurance companies, banks, government, traders, seed and fertilizer manufacturers, etc.

Big data helps in precision farming, also known as satellite farming; it works on the basis of observation and measurement from various sources. The primary goal is to use resources effectively and make informed decisions. All this is done to measure temperature, topography, soil fertility, salinity, water availability, chemical resources, moisture content etc.


The main application of data science in agriculture is smart farming that uses analytical technology. It helps overcome agricultural deficiencies and manage the supply chain, provides predictive insights, delivers real-time decisions and designs business models. This concerns management information systems specialized for:

  • Crop yield, stress, population
  • Fungal spots
  • Weed patches\
  • Soil texture and condition
  • Soil moisture and nutrients
  • Climate conditions
  • Precipitation and temperature
  • Humidity and wind speed
See also  agricultural reform

Smart farming will usher in a new era of farming techniques using many devices such as GPS, radar sensors, geographic information system, cameras, drones, cloud architect etc.