Leveraging Agronomy using Data Science

Leveraging Agronomy using Data Science

India has always been known as the land of agriculture, across the globe. Like how the country has a national animal, a national bird, and the likes, it also has a national profession as Agriculture. At least this was how it used to be, before all the technology and digital boom happened.

Farmers right now are at a crucial juncture in their professional trajectories. They could choose to follow thor traditional methods or could bend a little and take help from the vast technology around them.

Traditional methods are of course very good and a proven method for tremendous crop yield. However, the world today is moving at an ultra-speed. It is very important for a person, irrespective of the domain he is in, to catch up to it. Else, he/she would be lost in time, ideas and practices.

Being the backbone of the country’s economy, Agriculture gets affected every year due to a large number of factors, namely: natural disasters such as droughts, floods, fires, and man-made ones like land encroachment by corporates, tax increase, and the likes.

Though there are existing welfare schemes for handling the same, the efficiency and the effect of these schemes are quite low.

This is where technology and especially Data Science could prove immensely helpful.

Technology has seeped into every part of our society. Though it has its drawbacks, it has helped to a great extent. Data Science has been an evolving area of study and research for quite some time now. This methodology could be used in agriculture to improve the overall process, from end-to-end.

This article covers how Data Science could be used to leverage Agronomy. Agronomy is simply the science and technology of cultivating and using plants in agriculture and related areas.

Use of Data Science in Agriculture

The following are some major areas where data science proc\ves to be immensely helpful:

  • Mapping soil and crop – digitally

This is majorly helpful for farmers with huge areas of land. In such cases, it is difficult for them to analyze every unit of land and soil to check its texture, nutrient content, and so on. It would be a tedious task. This is where data science and technology comes into play. Digital maps could be but for the same. These when combined with satellite images and weather stations could give comprehensive data to the farmers regarding their land. It could also help them project future possibilities to a certain extent.

  • Predicting and analyzing weather changes

Climate change is an issue that has to be taken seriously. This in turn has started affecting the daily weather patterns. Farmers are affected and hurt by these changing weather patterns all around the year. Unpredicted weather changes damage crops and affects the soil sometimes causing them to erode. Some weather-sensitive crops are affected by the sudden changes in weather and thereby their yield is also affected.

There are various tools built on data science and technology which could be used to identify the patterns in the weather change and their related relationships. This could give important insights that otherwise stay hidden. 

  • Providing useful insights to help fight food scarcity

Food scarcity is a worldwide concern. By the looks of it, it could only grow further. The only way to minimize the growth rate or to optimistically stop it completely is to make sure the world has an excess of food availability. This excess should be calculated after considering every single citizen of the world.

Data science and technology could help in boosting the overall food output and yield through various feedback mechanisms and data modeling techniques. This could help in containing food scarcity at the global level.

  • Managing pests and other crop diseases

Pests are the main source by which a farmer’s profit margin takes a hit. Data science and modeling uses advanced algorithms that could help in estimating the pattern and behavioral changes of pests and related microscopic diseases.

  • Managing and recommending required fertilizers

Fertilizers to be used varies with the nature of the soil, the crops, the water availability, and the climatic conditions. It is difficult to keep track of all these factors, their relationships, and their ever-changing nature for large areas of farms. This is where data science comes into play. Advanced algorithms are built on this which can handle all possible permutations and combinations.

  • Managing water and irrigation through automation

Water is a prime issue in all parts of the world. It is scarce for drinking purposes let alone using for farming and irrigation. Data science and technology helps in reducing the usage of water based on the crop, weather, and other factors. The constant feedback provided by these data models helps in understanding the water situation in a much better sense. 

All these when implemented in conjecture, enables something called Smart Farming. Simply put, it is the automated management of the entire agricultural/farming process, from end-to-end.

Now, like with every other process, using data science for agronomy has its own set of challenges.

Data Science in Agriculture

Challenges faced while using Data Science for Agronomy

The following are a few of the major challenges faced while using data science to leverage and improve the agriculture process:

Skepticism in changing the age-old traditional habits
Data collection, cleaning and storing
Internet connectivity in rural areas

Real-Time Applications of Data Science based Agronomy

This topic is not just a theory. There are practical applications for this in existence right now. All those users are getting benefitted from data science in more than a single way.

The following are a select few of them:

  • In Egypt, farmers use advanced water sprinklers (with in-built data modelling technology) to water and irrigate their farms using the river Nile. These are used for medium-large farms as well.
  • There are a variety of apps developed for the sole purpose of helping in agronomy. Right from choosing the right feed for their livestock to choosing the right type of fertilizers, the correct level of pesticides, the right amount of water to be used to the right type of seeds for the land, everything is suggested by technology-driven applications. FamGraze is one such example.
  • Modern transportation facilities built on data modelling technology helps in the quick and efficient transport of goods between places. This is particularly helpful in the agriculture sector, as almost all of the yield goods under consideration are perishables. These need to be transported as efficiently as possible.

Conclusion

Being the only country in the world known for its agricultural practices, India should feel proud of itself. At the same time, the farmers must evolve with the changing technology and seek its help in improving their agricultural processes, practices and finally their yield.

Modern data modeling techniques using data science could be used prolifically to improve the agricultural output and thereby the living standard of the farmers and in-turn the whole country that rest of agriculture.

The Data Science program offered by Geeklurn could help in a deeper understanding of the topic discussed in this article.

Leave a Reply

Close Menu