Challenges To AI Adoption in Agriculture

by Olalekan Abdulsalami on 12 Feb 2021 • 54 comments
4 min read

Technology has redefined farming over the years and technological advances have affected the agriculture industry in more ways than one. Agriculture is the mainstay occupation in many countries worldwide and with rising population, which as per UN projections will increase from 7.5 billion to 9.7 billion in 2050, there will be more pressure on land as there will be only an extra 4% of the land, which will come under cultivation by 2050. This means that farmers will have to do more with less. According to the same survey, food production will have to increase by 60% to feed an additional two billion people. However, traditional methods are not enough to handle this huge demand. This is driving farmers and agro companies to find newer ways to increase production and reduce waste. As a result, Artificial Intelligence (AI) is steadily emerging as part of the agriculture industry’s technological evolution. The challenge is to increase global food production by 50% by 2050 to feed an additional two billion people. AI-powered solutions will not only enable farmers to improve efficiencies but will also improve quantity, quality and ensure a faster go-to-market for crops.

Agriculture is estimated to be a $5 trillion industry, research suggests that output could be even higher with greater efficiency provided by AI. So what is stopping Agricultural operations from fully deploying artificial intelligence through sensors, machine learning, and the like?

Below we have compiled some insights into the challenges hampering further AI adoption shared by some experts in the agriculture sector.

Brad Constantinescu, President Stone Soup Tech

“Despite the advances described above, agriculture is still a very hands-on business with a lot of room for improvement. The major challenge with AI adoption, as in most industries, is bridging the gap between farmers and AI engineers. Farmers encounter a variety of problems that AI can fix. They may not consciously see the problem or they may not know it is solvable. Nobody thought of putting activity trackers on cows to detect early signs of illness – before a company (Cowlar) did just that and improved dairy production. Meanwhile, AI engineers know little about agriculture, the problems and the opportunities in this field.”


John McDonald, CEO ClearObject


“Although AI has made inroads, agriculture remains a difficult sector to contain for the purpose of statistical quantification to realize AI’s value. According to Joseph Byrum in a 2017 blog post, even within a single field, conditions are constantly changing. Unpredictable weather, changes in soil quality, and crop disease can all affect the statistical analysis. At the same time, ecosystems vary wildly from continent to continent, meaning things like seed and fertilizer programs are rarely consistent globally. Therefore, AI and machine learning are still far from being able to predict critical outcomes in agriculture purely through the cognitive ability of machines.”


Rojer Royse, Royse Innovation Network 


“There have been technical challenges such as the lack of implementation of a rural broadband structure that remain to be resolved, but more importantly, as in other industries, AI has promised more than it has delivered. We aren't quite there yet, but it is only a matter of time, as the use cases have been clearly identified.”


Kirk Haney, CEO Radicle Growth


“AI is taking over the connected world, but the farm is not yet connected.  Once the farm is connected, whether satellites, 5G, LoRA or something else, this is when AI will transform ag.”


Ash Madgavka, Founder Ceres Imaging 


“The biggest challenge we see is making the insights that come from AI actionable. Farmers want to adopt AI but they aren’t going to waste their time if it’s not usable in the field.  AI powers new technology tools.  And like all tools, if they are not useful, they won’t be used. Agri-tech services need to focus heavily on making data actionable in a way that helps farmers prioritize issues in their fields in a more meaningful way.”


John Corbett, CEO aWhere

“Agriculture is finally switching from being driven by cultural traditions to one where business intelligence guides behavior. The warming atmosphere can completely switch ecological conditions for diseases (i.e., fungus, molds) and farmers continuing old practices are likely to suffer catastrophic consequences.”

Gary Morgan, Director MPT Innovation Group

“The major challenge in the broad adoption of AI in agriculture is the lack of simple solutions that seamlessly incorporate and embed AI in agriculture. The majority of farmers don’t have the time or digital skills experience to explore the AI solutions space by themselves. AI faces the same challenge as the war between AC and DC did at the turn of the 19th century; it became more about the solutions that are technology-powered, rather than the technology itself. AI solutions in agriculture will require new ontologies and common terminologies to be agreed upon globally. These new AI solutions will then have to be incorporated into existing and legacy infrastructure and systems that farmers already use (e.g. tractors, spreaders, or Farm Management software), through improved APIs, in order to seamlessly incorporate and embed AI within agriculture.”

Irrespective of the above challenges, one thing is sure: AI will significantly increase the efficiency of the farming industry. Undoubtedly, the need for quality AI solutions in agriculture will only grow. But we need to make sure there is cooperation between governments, science, and businesses in terms of proper investment and research.










References

Artificial intelligence in Agriculture [Retrieved fromhttps://www.disruptordaily.com/ai-challenges-agriculture on the 11th, February 20201] How AI revolutionizes agriculture and what the future holds [Retrieved from www.forbes.com on 11th, February 2021]

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