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Smart Technology: Applications in Agriculture

In this article on That’s Farming, Jana takes a look at smart technology and its applications in agriculture.

Smart technology has been the defining development of our time, as the iteration of newer, smaller, and cleverer devices – to say nothing of their integration and interconnectivity – has indelibly changed the way we communicate, learn, shop, and even grow our crops.

Agriculture is a bedrock of civilization; with climate change threatening our food pipelines in new and difficult ways, are seeing renewed interest as we seek to solve global problems. What part might smart technology play in that process?

Smart Sensors

A foundational element in contemporary agriculture is data.

Modern farmers can respond to changing information more quickly and effectively, by having more privileged access to information previously difficult to log.

This data might include soil saturation or minerality, specific growth rates for more aggressive crops, and even atmospheric information.

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The collection of this information is made possible by sensors – the development of which has been comprehensive and expansive over the past fifty years.

Today, complex sensor designs like that of the inductive proximity sensor render previously impossible actions possible – and in agriculture in particular, sensor data has become instrumental in improving crop yields, as well as minimizing losses.

Drones and Cameras

There is one sensor in particular that renders it easier for farmers to survey and supervise larger tracts of land, being the humble camera.

New camera technology has made CCTV and monitoring systems much cheaper and more accessible, with sympathetic developments in drone technology enabling farmers to survey their land from the sky.

IoT and Big Data

The above devices have existed and improved on their own merits for some time, but more recent development has enabled them to interconnect with one another and with central systems with much greater ease. The Internet of Things (IoT) is the interconnection of sensors and other smart devices via the internet.

This enables equipment to harvest and store information much more effectively, as well as to ‘talk’ to one another and react accordingly.

With IoT-enabled sensors and cameras, farmers have immediate access to volumes of complex data regarding weather, fertilizer, growth rates, and more. This is ‘Big Data’.

Machine Learning

Machine learning is arguably the biggest technological leap of the last generation, owing to its profound implications for a variety of industries and applications.

It is also indispensable for the management and interpretation of big data, leading to paradigm shifts in agricultural efficiency and agility.

Machine learning describes how algorithms self-optimize, iterating efficient processes and reacting to data sets intelligently.

Machine learning is the bedrock for artificial intelligence with wide-reaching capabilities.

In particular, machine-learning algorithms are well suited to sorting and interpreting information on our behalf, allowing us to ‘outsource’ key agricultural decisions to machines that can read complex data sets – whether relating to soil minerality, weather conditions, or other information.

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