Data analytics is transforming farming

Farmers are teaming up with data scientists to provide the necessary boost in food production by gaining a better understanding of soil, fertilisers, pest control and preventative measures to reduce waste.

Demand for food is increasing at an unsustainable rate, and our ability to increase supply is reducing using conventional methods. Meanwhile, McKinsey estimates that a third of food is lost post-harvest, which amounts to $940bn per year.

Forecasts indicate the global population will reach at least nine billion by 2050, and this will be compounded by a rising average calorie intake per capita. A UN report estimates we will need to produce 70% more food by 2050, a daunting goal to say the least. In addition, over-farming in some areas of the world has depleted the soil of nutrients, and this has begun to reduce crop yields. If not dealt with soon, this could result in food prices soaring, leaving the poorest in society the most vulnerable.

Farmers are turning to data scientists to increase yields and reduce waste, as big data’s ability to improve efficiency has been shown to be pervasive across all industries. Data analytics support more effective decisions, and its emergence could give rise to the most significant farming revolution since the advent of mechanisation.

Increasing yields

Gaining a better understanding of soil will be vital to increase yields. This can be achieved by digitising fields, a method by which soil samples are taken along with the GPS coordinates of each site. The samples are analysed in a lab and the results are compiled to produce a fertility map that displays the varying nutrient compositions of each region of the field.

Different strains of seeds are then planted in these regions according to the soil quality, and they are accompanied by the appropriate fertilisers. Sensors placed within the soil are able to provide real-time data on water content and nutrient concentrations. They can optimise plant growth by adjusting the levels of nitrogen, potassium and other building blocks essential for development.

At the end of the season, combine harvesters equipped with GPS trackers and yield monitors   provide data on yield variation within fields. By comparing this data to the historic data of other parameters measured throughout the season, farmers are able to determine the most effective approaches to implement during the following season.

However, some farmers have expressed concern over the security of monitoring systems and data protection. Internet of Things devices are notoriously susceptible to sabotage by hackers, which could leave farmers vulnerable to malicious behaviour among competing farms. In addition, there are no regulatory frameworks currently in place regarding data-sharing, and there are worries that crop-yield data could be used to manipulate market prices.

Reducing waste

There are numerous opportunities for waste to be reduced throughout the food cycle.

Drones and satellites can create images using infra-red lighting to highlight areas of field in need of attention before signs are visible to the naked eye. This could be vital in halting pest infestations before they become too severe. In additions, drones can patrol fields to warn farmers when crops are approaching maturity, so that yield is not lost due to over-ripening.

As big data develops, supply chain behaviour will be better understood and distributors will have access to more accurate demand forecasts. Machine learning algorithms will ensure that the precision of predictions is perpetually refined. In addition, sensors within food packaging can detect gases emitted by food spoils, enabling consumers to prevent waste.

There are myriad challenges to overcome in modern agriculture, but big data analytics offers a beacon of hope for a more efficient, waste-free society.