The global population is currently 8 billion. By 2050, it will increase to 9.7 billion. By the mid-2080s, it could peak at nearly 10.4 billion.
This prediction of explosive growth is accompanied by an all-important question: can we feed the world while preserving the world?
José Graziano Da Silva, Director-General of the Food and Agriculture Organization of the United Nations, warned that attempting to reach the 60% increase in food production required by 2050 through a “farming-as-usual approach” will put too much strain on our natural resources. Therefore, Da Silva urged, “We have no choice but to embark on a greener revolution.”
Not only does agriculture need to become more efficient, it needs to become more sustainable. But the “greener revolution” is coming, and it’s being led by the AgTech industry.
Key Takeaways
- Predictions that the global population will reach nearly 10 billion by 2050 are increasingly accompanied by questions about sustainable food production.
- A revolution in agriculture is necessary for us to meet the required 60% increase in food production by 2050 without depleting natural resources.
- AI in agriculture can mitigate harmful GHG emissions by optimizing the application of sprays and fertilizers, leading to more precise and sustainable farming practices.
- AI technologies enhance productivity, sustainability, and profitability by improving crop monitoring, disease diagnosis, and resource management.
Can AI in Agriculture Help Offset Harmful GHG Emissions?
The United Nations’ Food and Agriculture Organization has reported that 31% of human-caused GHG [greenhouse gas] emissions originate from the world’s agri-food systems.
Meanwhile, the Economic Forum claims that the increase in extreme weather—a direct result of global warming—is beginning to impact the production of everyday household staples such as potatoes, rice, and soybeans. Similarly, a NASA study suggests that
maize crop yields are projected to decline by 24% by 2030.
We’re caught in a vicious circle. Agriculture contributes to adverse environmental conditions that oppose its much-needed proliferation. One of the most significant issues is the mass application of synthetic fertilizers.
James Bennett, a PhD researcher at AgriFoRwArdS, told Techopedia:
“Without artificial nitrogen, our global food output would be 50% of what it is currently. However, the production and use of nitrogen fertilizer significantly contributes to global emissions.”
Bennett isn’t wrong. According to the United Nations Environment Program’s Frontiers 2018-2019 report, nitrogen’s capacity to threaten health, climate, and ecosystems makes it “one of the most important pollution issues facing humanity.”
The UN Environment Programme says it is three hundred times more potent at warming the atmosphere than carbon dioxide. And while a 2022 study published in Scientific Reports highlights agri-foods’ reliance on synthetic nitrogen fertilization, it stresses that the current quantities used are unsustainable. It is also important to note that fertilizers are not the only agricultural chemicals that impact the climate. Pesticides, herbicides, and fungicides also contribute significantly to GHGs.
Although we cannot currently dispense with artificial nitrogen or other sprays, Bennett believes that “using AI to optimize treatments we apply to our crop has the potential to reduce the impact that food production has on the climate.”
With the help of AI models, we can move away from general assessment and blanket treatments towards precision agriculture. Farmers will soon cease struggling to account for the extensive variation in any given field and start intervening with near-surgical accuracy.
Bennett said:
“Rather than spraying every square inch of a field with herbicides—which can negatively impact biodiversity—the AI tells the sprayer which weeds to spot-spray. This will result in a huge reduction in herbicide usage, which is a massive saving for the farmer and also beneficial to the environment.”
Use Cases, Challenges & Benefits of AI in Agriculture
So, how is AI used in agriculture? While running AI models on computers in labs might be relatively straightforward, the real challenge is deploying them across an entire farm or country. AgTech faces several challenges, including an unwillingness to embrace new technologies, insufficient infrastructure, and privacy and security issues.
Nevertheless, the AI in agriculture and farming market is set to grow from $1.7 billion in 2023 to $4.7 billion by 2028, indicating that technological development and steady integration are believed to be the future of productivity, sustainability, and profitability.
Let’s look at several AI tools that are already reshaping agricultural practices.
Real-life AI in Agriculture Examples in 2024
Although many factors affect crop performance, and there are many unknowns when it comes to reaching production targets, machine learning and image recognition can enable farmers to monitor a plant’s health and estimate disease severity to better gauge crop yields and ensure food security.
A study published in the journal Computational Intelligence and Neuroscience showed how AI trained on apple black rot images from the PlantVillage dataset achieved a 90.4% accuracy in identifying the disease. The study’s authors assert that this kind of “rapid, accurate diagnosis of disease severity will help to reduce yield losses.”
AI also offers the opportunity to optimize productivity and profitability by applying only what’s needed where it’s needed.
Systems like CropX gather thousands of data points from in-field sensors, satellites, and farm machinery. AI models then transform the input into information that helps farmers know when to irrigate and spray for disease.
According to CropX, their system can facilitate:
- 25-50% water savings
- 10-20% yield increase
- 9-13% fewer greenhouse gas emissions
- 10% reduction in energy costs
Blue River Technology, now owned by John Deere, invented the See & Spray technology, which utilizes advanced camera and nozzle control to detect and spot-spray weeds. On average, it uses 77% less herbicide than broadcast spraying but hits up to 98% of weeds.
Techopedia recently visited CNH Industrial’s Raven Innovation Campus in South Dakota and saw first-hand how an automated crop sprayer used AI to optimize the spraying process.
A highly automated crop sprayer that uses AI can stop spraying when it sees an empty field ahead and self-drive on very precise lines that keep it from running over crops. Radar even corrects the spray for the booms as they wobble.
The AgTech industry is also developing and deploying AI robots that could replace herbicides altogether.
Deepfield Robotics created Bonirob, a machine that distinguishes between weeds and crops. As reported by Farmers Weekly, Bonirob is not only a proficient weeder but can also “make plant breeding more efficient” and “monitor how well new crop varieties grow, [judging] whether they are resistant to pests and how much fertilizer and water they need.”
A final example of cutting-edge AgTech is Carbon Robotics’ Laser Weeder, a machine that, according to its creators, “can kill up to 99% of weeds, weed up to two acres per hour, and eliminate up to 5,000 weeds per minute.”
The Cost of Innovation: Is the Price Bearable for Farmers?
One of the disadvantages of AI in agriculture is the upfront cost of the latest innovations.
However, this need not be too much of a concern for farmers who invest in the Laser Weeder because, as the company claims, they will reduce “weed control costs by up to 80% and see a return on investment in one to three years.”
According to Marc Kermisch, CNH’s Chief Digital and Information Officer, although autonomous farming costs are “significant” right now, they are coming down and making the technology more accessible:
“On a cash crop farm (5,000 acres or more), it could lower expenses and improve yields. Smaller farms could still benefit from some autonomy, such as feed systems and aftermarket upgrade kits.”
The Bottom Line
The innovation of agriculture around 10,000 years ago led to the success of the human race. It contributed significantly to accelerating population growth, particularly in the last 500 years, and now food production is under unprecedented strain.
However, the current use of artificial intelligence in agriculture should encourage optimism. Greater levels of efficiency and sustainability make AI farming a sure path toward the bright future of agriculture.
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References
- Population | United Nations (Un)
- Feeding the World Sustainably | United Nations (Un)
- New FAO analysis reveals carbon footprint of agri-food supply chain | UN News (News.un)
- Access Denied (Weforum)
- Global Climate Change Impact on Crops Expected Within 10 Years, NASA Study Finds (Climate.nasa)
- EPSRC Centre for Doctoral Training in Agri-Food Robotics: AgriFoRwArdS (Agriforwards-cdt.blogs.lincoln.ac)
- FRONTIERS 2018/19 Emerging Issues of Environmental Concern (Wedocs.unep)
- Four reasons why the world needs to limit nitrogen pollution (Unep)
- Greenhouse gas emissions from global production and use of nitrogen synthetic fertilisers in agriculture | Scientific Reports (Nature)
- Pesticides & climate change: A vicious cycle | Pesticide Action Network (PAN) (Panna)
- Artificial Intelligence in Agriculture Market Size, Industry Research Report, Trends and Growth Drivers – 2032 (Marketsandmarkets)
- Factors Affecting Yield of Crops | IntechOpen (Intechopen)
- PlantVillage (Plantvillage.psu)
- Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning (Onlinelibrary.wiley)
- CropX Agronomic Farm Management System (Cropx)
- Our Products – Welcome | Blue River Technology (Bluerivertechnology)
- Bosch Bonirob robot set to make field work easier for farmers – Farmers Weekly (Fwi.co)
- Carbon Robotics’ LaserWeeder™ Selected as “Best AI-based Solution for Agriculture” In 2023 AI Breakthrough Awards (Businesswire)