Data Science Applications in Agriculture

Big Data and Data Science have already made waves in all industries, from IT to healthcare. Agriculture is another area where this revolutionary technology is being used to improve the lives of struggling farmers.

Agriculture is the most vital sector in any country, but it lacks support from banks in terms of farmer welfare schemes, loans and institutional attention. Those involved in the field receive very little support and face numerous disasters such as climate change, floods, droughts, unfair price-fixing policies, etc. With more and more new problems emerging every year, it is high time we resorted to to evolving technologies for solutions.

Smart farming

A pioneering application of technology and science newly applied to the field of agriculture is called Smart farming. It is a network of complementary and interdisciplinary facilities and technologies. Technologies such as Big Data, Internet of Things, Machine learning, Cloud computing and analytics are being applied to the agricultural sector to enable farmers to better understand the results of their actions and make better and learned decisions about agricultural practices.

The benefit of smart farming is not just limited to improving farming practices. The application of Data Science technologies has a significant impact in providing projective insights into agricultural operations and practices, as well as helping to redesign business models and deliver real-time decisions, thus significantly impacting the entire supply chain. Data analytics offers great opportunities to significantly improve the initial cost of producing an output ratio, optimize or reduce input usage, improve product yield, provide timely advice for necessary actions, and more.

Agriculture improvements

Satellite-based monitoring, built-in sensors on fields and crops, fertilizer need reports, wind direction forecasts, pest alerts, water cycles, GPS-enabled tractors and many more facilities act as a rich source of data for improving better farming practices. Monitoring and supervision of nutrient requirements and growth rates per plant is also made possible through the application of data science technologies. It also enables farmers to decide which crop to plant for the next crop based on the available data such as information on water availability, soil health, monsoon forecasts, etc.

Advantages in Marketing

With consumers eager to know where food comes from and how it is produced, processed and packaged, there is a need for transparency across the entire farm supply chain. The technologies that provide efficient and strategic farming solutions also include the use of app-based data extraction and generation, cloud data storage, machine learning, real-time data visualization, satellite monitoring, and so on.

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The use of these technologies enables production forecasting, output predictability, risk management, quality maximization and increased sustainability for farms, financial institutions and banks, insurance companies, seed production companies, farms, government agencies etc. Advice can be provided to farmers when supply exceeds demand or even the other way around. This can help control food inflation to a great extent.