Agricultural data science: harvest data

An enormous amount of invisible data is transmitted daily over cellular networks. Data can be of many types depending on source and type, which is why all types of industries rely on data so much.

Data science is a multidisciplinary field that brings together many subjects such as mathematics, statistics, computer science and business administration together. It combines different tools and techniques together, which are created for analytical purposes only. From collecting data to machine learning and presenting results to management, each step is to find meaningful insights from the data provided. The data is used as raw material to find solutions to business problems and predictive analysis of future problems.

One of the main public sectors that benefit from data science in agriculture. Although it is still in its modern phase, it has great ranges and applications.

Data science in agriculture

The agricultural scene is getting worse every year with:

  • Bad yield seeds.
  • Natural events
  • Water shortage and farming machinery.
  • Lack of financial aid.

All of this leads to overproduction or overproduction that farmers do not get at the right price and leads to suicidal and arable farmers. The problem is that innovations and technology are not being used to their greatest potential.

Various analytical techniques can assist farmers and their agricultural practices towards improvement such as:

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

For all these tools to work, one needs to have historical and current historical data to work on. All of this data can be collected from various sources such as government data sets or from sensors located near farms and machinery. Some of the rich sources of data are:

  • Satellite field field imaging
  • GPS sensors based on tractors and plows
  • Weather and climate forecast
  • Fertilizer requirements data
  • Pests and weeds
  • Data-based sensors from farms

Analysis of this data may be beneficial not only for farmers but also for insurance companies, banks, government, merchants, seed manufacturers, fertilizers etc.

Big data helps in precision agriculture, also called satellite cultivation; it works based on observation and measurement from various sources. The primary goal is to use resources effectively and make informed decisions. All of this is done by maintaining temperature, terrain, soil fertility, salinity, water availability, chemical resources, moisture content etc.

Smart farm

The main application of data science in agriculture is smart agriculture where analytics technology is used. It helps to overcome the shortcomings of the agriculture supply and control chain, gives predictive visions, provides real-time decisions and designs business models. It includes specialized management information systems for:

  • Crop yield, stress, population
  • Fungal spots
  • Herbal patches
  • Soil texture and condition
  • Moisture of soil and nutrients
  • Climatic conditions
  • Rain and temperature
  • Humidity and wind speed

Smart agriculture will start a new era of farming technology with the use of many devices such as GPS, radar sensors, GIS, cameras, drones, cloud architecture, etc.

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