Agricultural Data Science: Collecting Data

A large amount of invisible data is streamed through the cellular network every day. Data can be divided into several types according to sources and types, which is why all types of industries are very dependent on data.

Data science is a multidisciplinary field that combines mathematics, statistics, computer science, and business management. It combines various tools and techniques and was created only for analytical purposes. From data collection to machine learning to presentation of results to managers, every step is to find meaningful insights from the given data. The data is used as a raw material for finding solutions to business problems and predicting and analyzing future problems.

One of the major public sectors benefiting from agricultural data science. Although it is still in its infancy, it has a wide range of applications.

Agricultural Data Science

The agricultural situation is deteriorating every year for the following reasons:

  • Seeds with poor yield.

  • natural disaster

  • Lack of water and agricultural machinery.

  • Lack of financial assistance.

All of this has led to farmers ’inability to obtain adequate prices for underproduction or overproduction, and led to farmers’ suicide and arable farms becoming barren. The problem is that technological innovation and means cannot fully perform their functions.

Various analytical techniques can help farmers and their agricultural practices continue to improve, such as:

  • Big Data

  • Machine learning

  • Internet of Things

  • cloud computing

In order for all these tools to work, data with historical data and the current date is required to use it. All of this data can be collected from different sources such as government data sets, or from sensors near farms and machines. Some rich data sources are:

  • Satellite basic field imaging

  • Tractor and plough machine based on GPS sensor

  • Climate and weather forecast

  • Fertilizer demand data

  • Pest and weed infestation data

  • Sensor-based data from the farm

Analyzing these data will not only help farmers, but also insurance companies, banks, governments, traders, seed and fertilizer manufacturers, etc.

Big data helps precision agriculture, also known as satellite agriculture. It is based on observations and measurements from various sources. The main goal is to use resources efficiently and make informed decisions. All of this is done while maintaining temperature, topography, soil fertility, salinity, water availability, chemical resources, moisture content, etc.

Smart Agriculture

The main application of data science in agriculture is intelligent agriculture using analytical techniques. It helps to overcome the shortcomings of farming and controlling the supply chain, provide predictive insights, provide real-time decision-making and design business models. It involves a management information system dedicated to:

  • Crop yield, pressure, population

  • Fungal plaque

  • Weed patch

  • Soil texture and condition

  • Soil moisture and nutrients

  • Climatic conditions

  • Rainfall and temperature

  • Humidity and wind speed

Smart agriculture will use many devices (such as GPS, radar sensors, geographic information systems, cameras, drones, cloud architects, etc.) to create a new era of agricultural technology.


Source by Shalini M