Big Data Application – Agriculture Use Cases In Karachi Pakistan Dubai

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Big Data sets the tone for Agriculture Transformation! Check How?

Can you tell what is the most important thing for the survival of millions of people in this world?

“The world doesn’t need any more engineers. We don’t run out of airplanes, television sets or mobile phones…we ran out of food.”

In the world full of nearly 7.5 billion people to feed themselves, more focus is given to the technology and growth of the economy.

Big Data is helping and transforming multiple numbers of industries and changing the sectors with remarkable development.

About 100 years ago, industrialization began but the digitalization in industries is witnessed in this age. Digitalization has improved the face of agriculture and farming.

Also, the introduction of drone technology and robotics has modified the agriculture sector.

Why Agriculture needs Big Data?

Researchers have estimated that by 2050, the world’s population will reach 10 billion. This clearly means that food consumption will be double to fulfill the need for such a big number of population. Almost 40% of earth’s surface is already in use for agriculture and surprisingly a huge amount of production goes in the waste throughout the process. Therefore, Big Data is brought into the picture.

Big Data provides a helping hand for every problem and complexities in agriculture. It plays a key role in establishing an advanced and smart agricultural system. Farmers around the world may often get confused in decision making regarding the type of crop to be harvested. With the help of Big Data analytics, predictions are drawn from the previous year’s climatic conditions, the nutrients of the soil, rainfall, etc. These wise decisions with Big Data help to yield maximum production and help to grow the economic sector for the production of food.

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Big Data in Agriculture

According to DataFlair, these are the ways in which Big Data is helping the Agriculture sector.

1. Monitoring Natural Trends

Before Big Data existed, it was impossible to predict the significant risk factors like pest and crop diseases, and natural disasters like storms or extreme weather which can decimate entire harvests. Yes, experienced farmers can spot the signs of these factors but it’s often too very late. By feeding past and present data into a system and extracting insights through Data Science and valid algorithms can effectively boost future yields. This saves farmers from a lot of loss.

2. Accurate Crop Prediction

Sowing the seed and waiting for a plant to grow and see how the crop will yield is a long process. In recent years, Big Data as an accurate prediction provides help by predicting crop yields accurately without even planting a seed. An accurate algorithm is used to analyze the weather conditions and datasets of the crop from the last few years and predicts the best crop this year.

3. Agricultural Automation

Due to advancements in technology and big data, automated tools like farm bots, sprinklers, solar water pumps, and drips came into existence. Drones are to be fitted with advanced sensors to update their data, monitor crops, notifying the area needed for improvement. Robots are used in many parts of the world for planting kernels of corn and picking up the weeds which spoil the main crop.

4. Data-Driven Industry

The other good side of Big Data is that it is connected with external platforms for a considerable amount of data and insights. Farmers can use predictive analytic techniques to plan for and act as per the weather patterns, consumer demands, and trends. This data will help them to understand how the surrounding world affects the agriculture industry. What should they plant? What is the best time? Are the prices of supplies rising? and how does this affect profits? All these generate a need to create a data-driven industry that operates in new and innovative ways to make room for data-driven solutions.

5. Risk Assessment

In general business, management and planning teams often carry out a detailed risk assessment. But until now it is not practiced in the agriculture sector. With Big Data nearly every system, decision or event can be considered in the risk analysis plan. Every problem can be accounted for not just an appropriate solution but with the expected results. It makes sure that taking these actions won’t destroy the crop. Most importantly, they can use real-time data to ensure damage remains minimal.