A massive amount of invisible data is transmitted daily over cellular networks. Data can be of various types depending on the source and type, and this is why all types of industries are so dependent on data.
Data science is a multidisciplinary field that combines many subjects such as mathematics, statistics, computer science, and business management. It combines various tools and techniques together, which are created for analytical purposes only. From data collection to machine learning to presenting results to management, each step is finding 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 are benefiting from data science in agriculture. Although it is still in its early stage, it has great scope and applications.
DATA SCIENCE IN AGRICULTURE
The agricultural scene is getting worse every year with:
- Bad yielding seeds.
- Natural disasters
- Lack of water and agricultural machinery.
- Lack of financial aid.
All of this leads to under or over production for which farmers do not get a proper price and leads to farmer suicides and arable farms becoming barren. The problem is that technological innovations and the means are not used to the fullest of their capacities.
Various analysis techniques can help farmers and their farming practices to improve, such as:
- big data
- machine learning
- internet of things
- Cloud Computing
For all of these tools to work, you need to have current and historical data to work with. And all this data can be collected from different sources, such as government data sets or from sensors located near farms and machines. Some rich data sources are:
- satellite base camp images
- Tractors and plows based on gps sensors
- Climate and weather predictions
- Fertilizer Requirement Data
- Pest and weed infestation data
- Sensor-based data from farms
Analysis of these data can be useful not only for farmers, but also for insurance companies, banks, government, traders, seed and fertilizer manufacturers, etc.
Big data helps in precision agriculture, which is also called satellite agriculture; works on the basis of observation and measurement from various sources. The main objective is to use resources effectively and make informed decisions. All of this is done by maintaining temperature, topography, soil fertility, salinity, water availability, chemical resources, moisture content, etc.
SMART FARMING
The main application of data science in agriculture is smart agriculture, where analytics technology is used. It helps overcome gaps in agriculture and supply chain control, provides predictive insights, provides real-time decisions, and builds business models. These are specialized management information systems for:
- Crop yield, stress, population
- fungal patches
- weed patches
- soil texture and condition
- Soil moisture and nutrients.
- Weather conditions
- Rainfall and temperature
- Humidity and wind speed
Smart farming will usher in a new era of farming techniques using many devices like GPS, radar sensors, geographic information system, cameras, drones, cloud architecture, etc.