Agricultural Data Science: Harvesting Data

A massive amount of invisible data is streamed on daily basis through cellular networks. Data can be of several types depending on the source and type, and that is why all types of industries rely on data so much.

Data science is a multidisciplinary field, combining many subjects like mathematics, statistics, computer science, and business management together. It combines various tools and techniques together, which are created for analytics purpose only. From data collection to machine learning and presentation of results to the management every step is to find meaning insights from the given data. Data is used as a raw material to find solutions for business problems and predictive analysis of future problems.

One of the major public sectors which are benefitting from data science in agriculture. Though it’s still at its nascent stage it has great scopes and applications.

DATA SCIENCE IN AGRICULTURE

The farming scene is worsening every year with:

  • Bad yielding seeds.
  • Natural calamities
  • Lack of water and farming machines.
  • Lack of financial aid.

All this leading to under or over production for which farmers do not get a proper price and leading to farmer suicides and cultivable farms going barren. The problem is that technological innovations and means are not used to their fullest capabilities.

Various analytics techniques can help farmers and their agricultural practices towards betterment like:

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

For all these tools to work one need to have historical and present dated data to work upon. And all this data can be collected from different sources like governmental data sets or from sensors located near farms and machines. Some rich sources of data are:

  • Satellite base field imaging
  • Gps sensors based tractors and ploughers
  • Climatic and weather predictions
  • Fertilizer requirement data
  • Pest and weed infestation data
  • Sensors based data from the farms

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

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

SMART FARMING

The major application of data science in agriculture is smart farming where analytics technology is used. It helps to overcome shortfalls of farming and control supply chain, gives predictive insights, delivers real-time decisions and design business models. It involves management information systems specialized for:

  • Crop yield, stress, population
  • Fungal patches
  • Weed patches
  • Soil texture and condition
  • Soil moisture and nutrients
  • Climatic conditions
  • Rainfall and temperature
  • Humidity and wind speed

Smart farming will start a new era of farming techniques using many devices like GPS, radar sensors, geographical information system, cameras, drones, cloud architect etc.

Source by Shalini M

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