Global food production must increase by 70% in order to feed the world’s projected population of 10 billion people by 2050.

There is an relentless pressure on the farming industry, as it is, to become more efficient given tighter margins determined by the global market. Throw in ecological issues, such as river pollution and ecosystem disruption caused by intensive practices (of the kind needed to ‘feed the world’), and farming looks to be in a combustible state.

Sensors, Sensors, Sensors

The use of technology may go a long way to solving these issues.

The proposition of ‘Smart Farming’ recasts the industry as an optimization problem to be solved by analytics on data pooled from hundreds, if not thousands, of farm sensors. Sensors tracking movement of the cows, sensors in the land and in the air measuring temperature and moisture levels, sensors in the farm buildings and elsewhere all may produce data from which insights can be drawn, and which aids the running of the farm.

For example:

  • - Is there an ill cow in your herd? Apply behavioural analytics to movement data sourced from location sensors on cows. On big cattle stations, this question is not easily determined by a farmer with limited time on their hands. Sensors on animals can also be used in detecting theft of animals.
  • - Where should the cows be grazed to get the best milk yields? Take a multifaceted approach that accounts for current weather forecast data, combined with localised models of the farmland and its pastures, whilst allowing for recent data from environment agencies regarding pollution levels (e.g. from dairy nitrates) which may be used to bias the decision on ecological as well as monetary criteria.
  • - When should the farm gates be opened to allow cows to graze inside? When should sliding doors in cowsheds be opened to give more ventilation? Weather forecasting may again be used, but if cows subsequently move away from drafts caused by opening doors, then reverse the decision..

These are all smart decisions that can be made by smart technology. We propose SPARKL as a solution for all of the Smarts (cities, buildings, and so on), including Smart Farming.

Imagine this. The farmer installs sensors on the cows, and elsewhere on the farm. They plug SPARKL onto a stick and into a Raspberry Pi. SPARKL automatically detects the network of farm sensors. As it has been pre-configured for farming, SPARKL can immediately start performing analytics over the data, suiting the farmer as they want a solution based on zero configuration, and they can see what’s going on by logging into a web-based dashboard, either from a laptop or smartphone.

On the Edge

A key aspect of SPARKL is that it does analytics and makes decisions ‘on the edge’. It’s important to maintain the balance between ‘local’, where simple decisions should be taken locally, and ‘the cloud’ for more intensive number crunching. The decision to shut a cowshed door, based on local sensor readings and forecast data pulled (every 24 hours) by SPARKL over the farmer’s internet connection, can be taken locally without routing it via the cloud.

This is important, for example, if the internet connection is patchy, or, perhaps, for privacy, the farmer does not want to share all of their sensor data with the cloud. This is edge-based analytics and autonomics and its use is complementary to the cloud.

Technology is at the heart of these examples of advanced decision-making. In fact, the Internet of Things will bring a ‘decision support system’ flavour to farming. Smart Farming is inevitable, and once farmers see that technology is benefiting them in meeting production targets, they will trust it more. The hope also is that they will trust it to make decisions that are ecologically advantageous as well as giving them a good living in ‘feeding the world’.

Image courtesy of blog.imgtec.com