Digital tech is spreading into these fields, helping ensure farmers continue to meet the world’s growing need for food.
Mechanization has been key in enabling crop production on the scales we currently see, but it’s far from perfect. For example, the sheer size of many farms means fields are treated uniformly with water, fertilizer and pesticides, even though actual needs often vary between fields, or even between different parts of the same field. This ‘one size fits all’ approach wastes resources and damages the environment.
The IoT in agriculture
The good news for farmers (and everyone else) is that like in other industries, the Internet of Things (IoT) brings with it the power to do things better. Known as ‘Agriculture 4.0,’ this digitization of agriculture will empower farmers to tailor the way they care for their crops with enormous precision – without compromising on scale.
This is made possible by the huge amounts of data farmers are now able to collect, from environmental information to weather forecasts and real-time data on crop prices. Coupled with this, cloud-based predictive analytics and artificial intelligence (AI) mean they have the tools to draw meaningful insights from this data, and consequently make better decisions right across their operations. Ultimately, this can lead to greater yields and higher revenues – as well as increased environmental protection.
Robotic agricultural machinery has been one of the most noticeable manifestations of Agriculture 4.0 in recent years. These machines are now widely used, guided by Global Navigation Satellite System (GNSS) receivers, to till, plant, water, spray and harvest fields. As well as automating some relatively mundane tasks, these machines can help minimize soil compaction, cut resource consumption and save time by reducing overlap as they criss-cross the fields. Even more significantly, they can react dynamically to data from sensors, to provide the right field treatments at the right times in the right places.
Innovation reduces cost and improves accuracy
Recent innovation is driving down the cost of the technology, while enabling even greater accuracy. This is opening it up to many new applications within agriculture. Using existing satellite constellations, new-generation multi-band positioning hardware and GNSS correction data services tuned for compact, versatile and low-power use, centimeter-level accuracy is now a reality. And this gives farmers the opportunity to collect highly granular data about their fields – whatever size these are – and consequently manage them more effectively.
Another way to gather data is from the air. Take Yamaha Motor’s pioneering unmanned agricultural helicopter , which has proven popular around the world and treats millions of acres of farmland in Japan alone every year. Another example is the DJI Agras MG-1 drone, which can carry up to 10 kg of fertilizer, herbicide or pesticide and apply varying amounts with high precision. The MG-1 includes a ‘smart’ mode, which makes flight-planning and execution straightforward for farmers.
Driven by data and AI
As we’ve discussed, underpinning this more precise farming is data. A common source can be extensive networks of sensors that collect environmental information. This data is then sent to the cloud, where a typical use case involves superimposing it onto maps. These help farmers administer water, fertilizer or other resources more effectively, or even diagnose potential crop diseases.
Taking this a step further, artificial intelligence techniques can be used to analyze a mixture of datasets and pick out trends and patters that a human or traditional pre-programmed algorithm might miss. IBM Watson Machine Learning is one example of this type of technology that can be applied to agriculture.
And of course, sensors don’t have to be physically on (or in) the ground. Flurosat and Gamaya take to the skies with drone mounted cameras to enable farmers to measure the light reflecting off plants and split it up to 40 separate frequency bands encompassing both the visible and infrared electromagnetic spectrum.
Feeding this type of high-res data through appropriate machine learning tools can provide exceptionally valuable insights to farmers: are certain areas lacking in nutrients? Are pests starting to take hold somewhere? Are there early signs of crop disease? Where are weeds growing? And how should a farmer best tackle any issues that arise?
Exciting opportunities to help feed a growing population
This blend of increased data volumes and better analytical tools offers enormous potential to help farmers grow healthier crops using fewer resources – and reduce their impact on the environment. To reach their full potential, any solutions aimed at helping the agricultural sector better meet the world’s growing need for food must be robust and straightforward to implement.
At their heart will be high-precision positioning and wireless connectivity capabilities, which in turn means the GNSS correction data services and cellular or wireless networks will play an essential role. Put together, these have the power to revolutionize farming, unleashing new operational strategies that use data to drive ever-better decisions.