The near term potential for Artificial Intelligence in farming and food
What is the future potential for Artificial Intelligence in farming? Where will it lead us in the next 2–3 years?
I am firstly going to illustrate how the influence of AI has already expanded over the last 12–18 months or so, before looking ahead at the likely developments over the next 2–3 years and then thinking about where these trends are going to take us by the year 2030.
The last 12–18 months
As someone who is interested in Ag Tech, I take a keen interest in technology developments in medicine and healthcare. The healthcare industry, like agriculture, is dealing with living things and therefore a huge number of uncontrollable and sometimes unknowable variables. But healthcare also has a more innovative mindset than agriculture and R&D is often better funded. Healthcare is therefore a useful tracker for the sorts of developments which may appear further down the line in agriculture. Over the last 12–18 months in medicine, there has been a proliferation in the usage of AI to process and analyse test results, monitor patient progress and suggest options for treatment. In addition, a 2019 review in The Lancet stated that AI was already delivering equivalent diagnostic performance to human medical professionals when identifying diseases via medical imaging.
In broad acre arable farming, we are a little behind this and the use of AI to carry out and analyse test results, monitor crops or suggest treatment options is barely in the market, if at all. There is some interesting work being done with satellite and drone data to predict crop performance and support decision making, but this is often challenging to make use of on farms because it is so high level. Hummingbird Technologies and Agrimetrics are interesting UK companies to look at in this space. We are also seeing a huge growth in the amount of data being collected and in the number of connected sensors. Data on weather, soil, pests and disease is being collected more frequently and more accurately than ever before and it is beginning to drive decision making.
In broad scale agriculture we are still right at the start of the process. Smart farming in its current form is largely based around Machine Learning (ML). By ML I mean individual sensors, computers and algorithms gathering highly specific data and training themselves to achieve the specific task they have been set more efficiently. The next 2–3 years will see the aggregation of these datasets into an early form of AI for agriculture.
The next 2–3 years
2021 and 2022 will see a shift towards Artificial Narrow Intelligence for agriculture. This is where machines will get increasingly smart on a very specific set of tasks, such as autonomously detecting weeds, pests or diseases in crops. Lots of work that is going on in these areas in academia or commercial R&D will burst into the mainstream.
Farmers will start to get used to having their decision making supported by machines which are autonomously gathering, analysing and presenting data to them. It will begin to become normal for farmers to use AI to blend layers of data together to make better decisions. Farming will remain a relationship business, but the most forward thinking farmers will begin to realise that one of their most important relationships will be with the technologies that they are using to make day to day decisions on their farms. To repeat an earlier theme, it will become more important that the farmer is able to ask the right questions than it will be that they know the answers to all of the questions.
Towards the end of this time period, we will begin to see the first signs of system change in farming as ML algorithms begin to discover patterns in agriculture which have previously been hidden from human operators. This could change our thinking about which crops to grow in which areas, how to minimise the risk of pest damage, which weeds we need to focus our attention on first, how to reduce our chemical usage, increase our carbon sequestration capacity, maximise biodiversity in our arable fields and many more areas besides.
In three years’ time, AI powered robots will have a strong foothold in the arable market, somewhere between 5–10%. It will still only be the early adopters who are using this technology across a broad acreage. The vast majority of the industry is likely to be operating a farm that looks very similar to the farm they are operating today, but within this timeframe the broader industry will be starting to take decisions with the support of AI tools, such as Small Robot Company’s AI driven Advice EngineTM, even if they are not yet using robotics. However, the key shift over the next three years will be around the credibility of these new technologies as a driving force for the future of farming. Questions about their technical feasibility will no longer be valid. The conversation will have shifted to one of prioritisation and the most appropriate business models for enabling these technologies to become ubiquitous throughout the industry.
Artificial Intelligence will transform the farming industry. Robots are the tools to enable this transformation to happen at scale.
In my next blog, I will take a look further into the future and build out a vision for what an AI led future for farming could look like in 2030.
