Can you imagine an industry that involves more risk than agriculture? You reap what you sow, they say. But what they forget to add is “if you’re lucky.” When the weather strikes or crops get affected by disease, farmers can hardly talk about yields. Or when a global pandemic hits, all of a sudden it gets harder to manage various processes because most are not digital.
At the same time, the global population is growing, and urbanization is continuing. Disposable income is rising, and consumption habits are changing. Farmers are under a lot of pressure to meet the increasing demand, and they need a way to increase productivity. Thirty years from now, there will be more people to feed. And since the amount of fertile soil is limited, there will also be a need to move beyond traditional farming.
We need to look for ways to help farmers minimize their risks, or at least make them more manageable. Implementing artificial intelligence in agriculture on a global scale is one of the most promising opportunities.
AI can potentially change the way we see agriculture, enabling farmers to achieve more results with less effort while bringing many other benefits. However, AI is not a technology that works independently. As the next step on the way from traditional to innovative farming, AI can supplement already implemented technologies.
Agribusinesses need to know that AI isn’t a panacea. However, it can bring tangible benefits to small everyday things and simplify the lives of farmers in many ways. So how can we use artificial intelligence for sustainable farming? What are the opportunities of AI in farming and how can AI help us tackle existing challenges?
In this article, you’ll learn:
- Why adopting AI is such a challenge for farmers
- How AI can be useful in agriculture
- Applications of AI in solving farming challenges
- What problems farms can face while adopting AI
- How AI should be combined with other technologies
- Reality vs expectations of artificial intelligence for sustainable farming
Why adopting AI is such a challenge for farmers
Farmers tend to perceive AI as something that applies only to the digital world. They might not see how it can help them work the physical land. This is not because they’re conservative or wary of the unknown. Their resistance is caused by a lack of understanding of the practical application of AI tools.
New technologies often seem confusing and unreasonably expensive because AgriTech providers fail to clearly explain why their solutions are useful and how exactly they should be implemented. This is what happens with artificial intelligence in agriculture. Although AI can be useful, there’s still a lot of work to be done by technology providers to help farmers implement it the right way. Agriculture software development Make smarter decisions resulting in higher profitability and sustainable growth
Agriculture software development
Make smarter decisions resulting in higher profitability and sustainable growth
How AI can be useful in agriculture
Agriculture involves a number of processes and stages, the lion’s share of which are manual. By complementing adopted technologies, AI can facilitate the most complex and routine tasks. It can gather and process big data on a digital platform, come up with the best course of action, and even initiate that action when combined with other technology.
The role of AI in the agriculture information management cycle
Source: MDPI – From Smart Farming towards Agriculture 5.0
Combining artificial intelligence and agriculture can be beneficial for the following processes:
Analyzing market demand
AI can simplify crop selection and help farmers identify what produce will be most profitable.
Farmers can use forecasting and predictive analytics to reduce errors in business processes and minimize the risk of crop failures.
By collecting data on plant growth, AI can help produce crops that are less prone to disease and better adapted to weather conditions.
Monitoring soil health
AI systems can conduct chemical soil analyses and provide accurate estimates of missing nutrients.
AI can monitor the state of plants to spot and even predict diseases, identify and remove weeds, and recommend effective treatment of pests.
AI is useful for identifying optimal irrigation patterns and nutrient application times and predicting the optimal mix of agronomic products.
With the help of AI, it’s possible to automate harvesting and even predict the best time for it.
Learn how platform-based crop management software can promote sustainable farming practices
Applying AI to solve farming challenges
AI enables better decision-making
Predictive analytics can be a real game-changer. Farmers can collect and process significantly more data and do it faster with AI than they would otherwise. Analyzing market demand, forecasting prices, and determining the optimal time for sowing and harvesting are key challenges farmers can solve with AI.
That said, AI can also gather soil health insights, provide fertilizer recommendations, monitor the weather, and track the readiness of produce. All of that enables farmers to make better decisions at every stage of the crop cultivation process.
See the benefits of GIS-based agriculture farm management software for effective field operations and planning
AI brings cost savings
One particular farm management approach — precision agriculture — can help farmers grow more crops with fewer resources. Precision agriculture powered by AI could become the next big thing in farming. Precision farming combines the best soil management practices, variable rate technology, and the most effective data management practices to help farmers maximize yields and minimize spending.
AI can provide farmers with real-time insights from their fields, allowing them to identify areas that need irrigation, fertilization, or pesticide treatment. Also, innovative farming practices like vertical agriculture may help increase food production while minimizing the use of resources. The result is reduced use of herbicides, better harvest quality, higher profits, and significant cost savings. See how our prototype of a drone farming solution has given our client a competitive edge
See how our prototype of a drone farming solution has given our client a competitive edge
AI addresses labor shortages
Agricultural work is hard, and labor shortages in this industry are nothing new. Farmers can solve this problem with the help of automation. Driverless tractors, smart irrigation and fertilizing systems, smart spraying, vertical farming software, and AI-based robots for harvesting are some examples of how farmers can get the work done without having to hire more people. Compared with any human farm worker, AI-driven tools are faster, harder, and more accurate.
Problems farms can face while adopting AI
Considering the benefits of artificial intelligence for sustainable farming, implementing this technology may look like a logical step for every farmer. However, there are still some serious constraints.
Lengthy technology adoption process
Farmers need to understand that AI is only an advanced part of simpler technologies for processing, gathering, and monitoring field data. AI requires a proper technology infrastructure for it to work. That’s why even those farms that already have some technology in place can find it difficult to move forward.
This is also a challenge for software companies. They should approach farmers gradually, giving them simpler technology first, such as an agriculture trading platform. Once farmers get used to a less complicated solution, it will be reasonable to step it up and offer something else, including AI features.
Lack of experience with emerging technologies
The agricultural sector in developing countries is different from the agricultural sector in Western Europe and the US. Some regions could benefit from artificial intelligence agriculture, but it may be hard to sell such technology in areas where agricultural technology is not common. Farmers will most likely need help adopting it.
Therefore, tech companies hoping to do business in regions with emerging agricultural economies might need to take a proactive approach. In addition to providing their products, they’ll have to provide training and ongoing support for farmers and agribusiness owners who are ready to take on innovative solutions.
Privacy and security issues
Since there are no clear policies and regulations around the use of AI not just in agriculture but in general, precision agriculture and smart farming raises various legal issues that often remain unanswered. Privacy and security threats like cyberattacks and data leaks may cause farmers serious problems. Unfortunately, many farms are vulnerable to these threats.
How AI should be combined with other technologies
As we’ve mentioned, AI can’t exist without other technologies already in place such as big data, sensors, and software. Likewise, other technologies need AI for them to function properly. For example, in the case of big data, the data itself is not particularly useful. What actually matters is how it’s processed and whether it’s relevant.
The time, the place, and the selection criteria all determine whether AI recommendations based on a set of data are going to be helpful. That’s why it’s also important to have good data engineers and data analysts to make AI technology work. Let’s talk about the uses of artificial intelligence in farming in more detail.
Big data for informed decision-making
The real goal of producing and collecting data is putting it to use. In farming, data analytics can result in massive productivity increases and significant cost savings. By combining AI with big data, farmers can get valid recommendations based on well-sorted real-time information on crop needs. This, in turn, will take away the guesswork and enable more precise farming practices such as irrigation, fertilizing, crop protection, and harvesting.
IoT sensors for capturing and analyzing data
Farmers can use IoT sensors and other supporting technology (e.g. drones, GIS, and other tools) to monitor, measure, and store data from fields on a variety of metrics in real time. By combining AI farming tools with IoT devices and software, farmers can get more accurate information faster. Better data means better decisions and less time and money spent on trial and error.
Learn how our vertical farm lighting solution uses artificial light to automatically illuminate plants and monitor their growth
Automation and robotics for minimizing manual work
Artificial intelligence combined with autonomous tractors and IoT can solve one of the most common problems in farming: a shortage of labor. These technologies are also potentially cost-effective because they’re more accurate and thus reduce errors. Taken together, AI, autonomous tractors, and IoT are the key to precision agriculture.
Another less common but rapidly growing technology is robotics. Agricultural robots are already being used for manual work, such as picking fruits and vegetables and thinning lettuce. The advantages of robots over farmworkers are considerable. They can work longer, are more precise, and are less prone to error.
Reality vs expectations of artificial intelligence for sustainable farming
The benefits of AI in agriculture are undeniable. Smart farming tools and vertical farming systems can perform small, repeatable, and time-consuming tasks so farm workers can use their time for more strategic operations that require human intelligence. However, it’s important to realize that unlike a tractor, one can’t just buy AI and start it. AI is not something tangible. It’s a set of technologies that are automated through programming.
Artificial intelligence is essentially a simulation of thinking; it’s learning and problem-solving based on data. AI is just the next step in the development of smart farming, and it needs other technology to actually work. In other words, to reap all the benefits of AI, farmers first need a technology infrastructure. It will take some time, possibly even years, to develop that infrastructure. But by doing so, farmers will be able to build a robust technology ecosystem that will stand the test of time.
For now, technology providers need to think about a few things: how to improve their tools, how to help farmers address their challenges, and how to easily and understandably convey that machine learning helps solve real struggles, such as reducing manual work. The future of AI in agriculture is bound to be fruitful.
Looking for ways to implement AI in your farming operations? Let’s talk about it! The Intellias team is here to consult you on technological solutions for your fields.