How Data Analytics is Used for Agriculture: Insights from the Field

How Data Analytics is Used for Agriculture: Insights from the Field

Agriculture and rural development | European Institute for Gender Equality

The world of data analytics is growing by leaps and bounds. It seems like every day, a new application for data analytics is being developed. One area where data analytics is making a huge impact is in the agricultural industry. Farmers are using data analytics to improve their yields and make more money.

In this blog post, we will take a closer look at how data analytics is being used for agriculture and the benefits that it brings to the table.

Satellite and drone images

Satellites can provide an array of useful data such as the tracking or surveillance of agriculture crops and the determination of field boundaries; identifying a variety of plants and weeds, and others.

AgriTech companies and farmers are now utilizing information from traditional satellite constellations, including Sentinel.

Sentinel has several benefits – providing a data set for free, and images can be sent on different frequencies – including a range ranging RGB and Infrared. Sentinel, however, carries some downsides : poor image resolution and very low frequency of data update.

Adaptation of the African agriculture to climate change

More efficient crop prospecting

One way the agricultural data are processed is in bioprospection — an agricultural research process.

Bioprospecting is an analysis by which plants develop the molecules that help their specific market function better.

For example, a farmer wanted sweeter corn for sale at a market. They would examine several seeds to see what varieties produce molecule-producing sweetened corn. The data-analysis technology allows for a better and faster method of bioprospecting. This could result in higher yielding crops and reduce wasted resources and increase revenue.

Data science has evolved to serve agricultural business processes by helping farmers adopt smart farming and embrace data driven solutions. Basic data analysis and big data analytics can contribute to the agricultural supply chain by placing emphasis on precision agriculture market.

More effective supply chain management

We shouldn’t forget what food means. It is important for agriculture to be more efficient and accurate in getting food to consumers. Centrally underlying this conversion from farming to stomach is the supply chains. The supply chain translates into a route that runs from one production facility to the consumer. It would cover everything from load stations, trucks, ships and networked warehouses. The fact that agricultural products can be damaged pretty easily reflects an interest in improving supply chains.

IoT sensors

Internet-based devices have been used in Agricultural applications for many years. The GPS technology was developed in the early 90s with the United States Navy releasing the GPS system for military purposes. During these years, farming has turned their attention to agricultural devices and sensors previously reserved for military and scientists. Already hundreds have been developed for use in fields that can easily be incorporated. In addition, they provide a wide array of information about soil structure.

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Cloud computing and big data in agriculture

The agriculture industry was mostly dependent upon natural forces but not now. Cloud computing coupled with big data provided farmers with sufficient data for better decisions. Cloud computing is democratizing the availability of huge computational resources as datacenters are now a paid-for option. The system also allowed bringing together knowledge repositories containing data involving weather, irrigation practices, plant nutrients and other agricultural technologies.

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