In today’s world, discussion about "the age of Big Data" and "information overload" is commonplace. These offhand remarks underscore a reality in which we have access to more data than ever before, with the amount increasing on a minute-to-minute basis.
This immense amount of data brings benefits to a range of different industries. Marketers can connect products and services to the right consumers with increased accuracy, while e-commerce platforms can pack and deliver goods with incredible efficiency.
But the benefits of these massive amounts of data aren’t exclusive to blue-collar workers in urban skyscrapers, or managers in open-air factories. Large quantities of data are immensely useful for the agriculture industry as well.
Let’s walk through some of the central ways in which agriculture uses data analytics and data science to improve the efficiency and effectiveness of their work. Here’s what we’ll be explaining through this article:
- How data is used for more efficient prospecting of crops
- How data is used to more accurately monitor land and resources
- How data is used to manage the agricultural supply chain
- How data is used to be more responsive to environmental conditions
Use Data Analytics to Help Dot’s Agricultural Endeavours in the 21-Day Data Challenge!
The explosion in the use of data analytics and data science across different industries means that it’s quickly becoming a much sought-after skill. Professionals that know how to expertly navigate their way around a Python script or a data visualization technique will find their value as an employee increasing exponentially.
If learning how to effectively use data will increase your professional worth, why doesn’t everyone learn? Because learning a complex new skill like data analytics can be very difficult. One of the ways to make this learning process a bit easier is to try and establish a daily study routine.
Lighthouse Labs has made that process more intuitive and user-friendly with the 21-Day Data Challenge. There, participants work through 21 days of fun, engaging data challenges to improve their Python and data science skills. The storyline follows the protagonist, Dot, as they try to adapt to a rural life off-the-grid.
Use data to help Dot with challenges like growing vegetables, buying a cow, and weigh crop prices. It’s a great microcosm of how data can be used in the agriculture industry!
Now, let’s take a deeper look at how data’s used in the agriculture industry.
More Efficient Prospecting of Crops
One of the ways that data is used within the agriculture industry is for something called bioprospecting. Bioprospecting is a process in which it is determined which plants produce molecules that make them more beneficial for specific markets.
For example, let’s say a farmer wanted to produce a sweeter kind of corn to sell to a market. They would analyze different seeds to see which crops would produce the molecules for sweeter-tasting corn.
By leveraging data analytic techniques, bioprospecting can be done much more accurately and efficiently. This can lead to improved crop yields, reduced waste, and greater revenues.
Another data science tool that can be used within bioprospecting is machine learning. The agricultural industry can use machine learning algorithms to automatically sort seeds and analyze which are most useful. This exponentially improves the efficiency of bioprospecting methods.
More Accurate Monitoring of Land and Resources
The agricultural industry is basically useless without the material resources involved: the land, crops, and animals. It’s not like this is a process that can be entirely digitized and automated. We’ll probably never be able to push a button on a computer and have an ear of corn pop out of a 3D printer. Or at least not for hundreds of years.
So how do we use digital skills to improve the effectiveness and efficiency of agriculture? One way is to use data tools to gather more accurate information on land and crops. Data can be used to more accurately monitor these resources, so that industry experts can respond to conditions quicker and streamline their processes.
One way this is accomplished is by using cloud computing methods to monitor agricultural properties. Databases of satellite images are accessed and analyzed for information on the property and its conditions. drones and Internet of Things tools are also used simultaneously to increase the quantity and quality of information.
The Internet of Things, or the interconnected systems of gadgets and devices that are connected to the web, has many applications for data-driven agriculture. IoT gadgets can be used to monitor the behaviour of animals on farms more closely.
Aspects like fertility, milk production, and behavioural abnormalities can all be tracked, then responded to using data analytics.
More Effective Supply Chain Management
Let’s not forget that food is meant to be eaten. The whole point of using data to make the agriculture industry more efficient and accurate is so food can more effectively get into a consumer’s belly. Central to this conversion between farm and stomach is the supply chain.
The supply chain is what we refer to as the steps in the pathway between a producing facility and the consumer. For the agriculture industry, this would encompass everything from loading facilities, to networks of trucks and boats, to warehouse and store deliveries.
Because of how agricultural products can go bad fairly quickly, there’s an invested interest in making the supply chain as efficient as possible. This is where data analytics comes into play.
Data analytics is used to monitor efficiency in the supply chain. By monitoring, collecting, analyzing, and responding to data, products can get to consumers much more efficiently and cost-effectively.
New technologies like blockchain are also making supply chain data even more transparent and effective. Blockchain is a method of storing data in which everything is transparent and unalterable, leading to clearer communication between stakeholders and greater understanding of long, complex chains.
More Responsive to Environmental Conditions
One of the main problems for the agricultural industry is the environment. We can’t control the weather (yet?), which means that agricultural output is largely dependent on a region’s climate. It can be very difficult to accurately predict what the weather’s going to look like far in advance, which makes agricultural work difficult.
Data is looking to shift that situation a bit. Agricultural workers can use data analytics to more closely analyze and navigate shifting environmental conditions. Increasing quantities of available data means much more accurate information about the climate and environment.
Using these huge amounts of data, professionals in the agricultural industry can then make informed decisions on how to best manage resources, based on the proven trends they’ve analyzed.
Now that you’re impressed at these interesting applications of data, want to learn more about how to become a data science expert?