HCR Law experts explain what you need to know about artificial intelligence and machine learning in agriculture.
In an interview with Open Access Government, HCR Law's Rory Hutchings, Steve Thomas, and Bryn Thomas discuss the growing importance of artificial intelligence (AI) and machine learning (ML) in agriculture. They point out that global agriculture faces the challenge of producing enough food to meet the demands of a projected population of 10 billion people by 2050. This is in line with one of the key priorities of the agricultural sector in the agricultural sector. The goal is to ensure that the sector maintains its current level of sustainability. By adapting to changing technology. (1)
A report was published in March 2024 highlighting the positive results achieved under the EU's long-term rural vision for 2021 and beyond. The report also provided suggestions for future efforts. Janusz Wojciechowski, Agriculture Secretary from 2019 to 2024, emphasized the importance of working with rural communities and listening to their needs. He believes in recognizing their needs and finding solutions to address them. (2) The question arises whether AI and ML can play a role in achieving these goals.
What is the role of technology in global agriculture?
Regarding AI and ML in agriculture, the first thing to note is that the world's population is projected to start declining after 2050, and the need to produce food for perhaps 10 billion people will be a short- to medium-term challenge. That means there is. There is little doubt that global agriculture can produce enough food to meet the planet's needs, and technology will play a major role in increasing yields and reducing input costs.
A more nuanced question is whether we can produce food without irreversibly damaging the Earth's ecosystems and reducing our ability to produce food beyond 2050. It may also mean a change in our expectations about food production and what we consume. For example, red meat has a relatively inefficient protein conversion rate, while chicken and fish have significantly better protein conversion rates. Similarly, a move toward the use of insects as food for both humans and animals seems likely. Technologies for producing “grown” meat and protein equivalents are also growing.
AI and ML will be key elements of the technology needed to deliver sustainable global food production.
Why is soil so important to the future of food production?
Without healthy soil, the ability to produce crops is reduced. Efficient crop production and increased yields can only be achieved with healthy, biodiverse soils containing sufficient amounts of organic matter.
Will AI and ML soon replace traditional soil improvement methods?
AI and ML are likely to produce more accurate measurements, and analysis of the data allows for a more structured approach to improving soil health. Ultimately, however, soil health is highly dependent on the physical input of organic matter.
What are the obstacles to using AI to measure soil health?
Like other innovations, using AI to measure soil health is expensive and not easily available to small farms. Farmers and land managers need knowledge and expertise to get the most out of AI, which takes a lot of time. Crop production is highly dependent on weather and climate change, so accurate and reliable soil analysis requires frequent data collection. The ability to predict and quickly respond to changing conditions is essential to agricultural efficiency and profitability. Can AI match the lived experience that farmers have had for years as part of their jobs?
Given the incentives for sustainable agriculture, AI systems must also consider the environmental impacts of achieving the highest crop yields, such as long-term overuse of fertilizers and soil erosion. Further development and research is needed to improve AI models and ML to improve soil analysis performance while promoting sustainable land management practices.
So what will happen to AI and agriculture in the future?
Agriculture, like any other business, needs to adapt to the times, adopt new technologies, and diversify to the extent practical. This includes investing in available technology, understanding it, and using it to maximum effect. AI could be a solution to the UK’s farm worker shortage post-Brexit, helping drive production while limiting costs associated with visas and work permits for migrant farm workers.
While technology continues to evolve and agricultural professionals continue to adapt to it, we will see a healthy balance between old and new in leveraging farm workers and providing jobs for them.
If farmers are no longer needed on the farm, their role could be repurposed elsewhere, perhaps in testing such technology. They know their job best.
Farmers and landowners can harness the power of AI to make informed decisions based on data, use resources more efficiently, and improve soil health while building a more sustainable food system. Can we adopt practices?
The answer is yes. The real impact of AI is to help farmers and policy makers understand and analyze data. This would enable more targeted interventions and improve our understanding of, for example, carbon sequestration. There is an opportunity for AI to support the agricultural industry, improve soil health, and provide a deeper understanding of the agricultural industry's policy context.
Decisions about agricultural policy between now and 2050 must be based on evidence and science, not political whims or stereotypes. If AI and ML focus on data and science, and politicians stay out of the way, the outcome could be higher yields and more sustainable food production systems.
References
- https://commissioners.ec.europa.eu/janusz-wojciechowski_en#:~:text=Janusz %20Wojciechowski%20%2D%20Europe%20Commission
- https://ec.europa.eu/commission/presscorner/detail/en/IP_24_1727
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