Tech

AI in Agriculture: How Artificial Intelligence Is Transforming Farming

Published:

August 28, 2024

Key Takeaways:

The agricultural AI market was valued at about $1.6 billion in 2023 and is expected to reach $7.15 billion by 2032.
The key benefits of using AI in modern agriculture include data-based decision-making, remote monitoring, and cost-saving automation.
AI tools and systems can help at any stage of the farming cycle, from irrigation planning to weather prediction to pesticide application.
Upfront costs and difficulty in adopting new processes are some common challenges with AI implementation, however, they can be addressed.

One of mankind's oldest inventions is combining with one of the newest innovations — and it could be beneficial to both!

Just a few years ago, the idea of AI in the farming industry would have sounded like science fiction. Now, it is well on its way to becoming a reality. Intelligent agriculture systems are already being deployed in large-scale factory farms, and smaller operations are starting to embrace the benefits as well.

The growth is real. Already, in 2023, the agricultural AI market was valued at about $1.6 billion USD. By 2032, less than a decade from now, it's expected to reach $7.15 billion. That would be annual growth of more than 20% per year.

So how does AI help in agriculture? What problems can implementing AI and IoT (Internet of Things) devices solve? What will the future of ag AI be like? Let's take a look.

Understanding Current "Artificial Intelligence"

A little clarification is needed before we begin: What we currently call "Artificial Intelligence" or AI is a collection of various technologies, rather than a single unified system. Further, and more importantly, modern AI is not truly intelligent. Many media reports tend to gloss over that aspect and make AI out to be more intelligent or independent than it actually is.

Current AI runs on statistics and probability. Computers are extremely good at taking in vast amounts of data, and then deriving trends and averages from that data. "AI" is simply based on making mathematical predictions based on those derived numbers.

So, as a simple example: say an AI system is hooked up to monitor a particular irrigation water pipe. It records the flow rate and water pressure minute-by-minute for months, so it develops a good baseline for that pipe's performance. Then the sensor detects a sudden drop in water pressure. This falls far outside the baseline, so the AI sends an alert to human operators that there's most likely a leak prior to that sensor. If multiple sensors on the pipe are cross-linked, it would also be able to check the pressure on upstream sensors for abnormalities and thus predict roughly where the leak should be.

That's current agricultural AI in a nutshell. It can't run your farm for you, but it can provide a wealth of data and moment-by-moment monitoring which would be beyond a human's capabilities.

Overall Benefits of AI in Agriculture

As a broad overview, the benefits of implementing AI in the agricultural sector can be broken down into three basic categories.

Data-Based Decision-Making. As mentioned above, the true power of current AI is in crunching a lot of numbers and spitting out reports that humans can easily understand. This allows modern farmers to operate in a much more information-rich way, making decisions based on hard numbers and derived predictions. As mentioned above, the true power of current AI is in crunching a lot of numbers and spitting out reports that humans can easily understand. This allows modern farmers to operate in a much more information-rich way, making decisions based on hard numbers and derived predictions. 
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As a few examples:
- Predicting average weekly or monthly rainfall 
- Predicting crop value based on market trends 
- Suggesting plantings for optimal harvests or soil health 
- Recommending pesticides based on plant health

Visual Monitoring. AI systems can also integrate camera feeds, and derive intelligence by analyzing what it "sees." An AI can be trained to know what healthy plants look like, and so be able to send alerts if they show signs of diseases or pests. It may even be able to identify specific pests if it gets a clear view. The same is true for animals, as well. Cameras could watch a herd of cattle, knowing their typical behavior, and send alerts if that behavior changes substantially — such as a cow going into labor. This will allow farmers to have much better oversight of their day-to-day operations, with minimal extra labor.
Cost-Saving Automation. Another major benefit of AI will come in the form of automation, and the cost-savings it brings. A one-time installation of solar-powered cameras in a field for 24/7 crop monitoring could do the work of a dozen humans attempting the same task. Some groups are even working on "smart" harvesting machinery that could identify ripe harvests automatically, leaving unripe plants to mature. Between cost-savings in automation and harvest optimization through better irrigation, fertilization, etc., studies have estimated that AI could reduce the costs of farming by at least 20% as the technology matures. With the global human population projected to be nearly 10 billion by 2050, these sorts of agricultural optimizations and cost savings will be necessary to keep everyone fed!

Agricultural Information Management with Cropler

Specific Applications of Intelligent Agricultural Systems

This is a non-exhaustive list, and new applications for AI in the farming industry are being explored constantly. However, let’s take a look at the most common options.

Optimizing Automated Irrigation Systems. Irrigation is often a hit-and-miss process with a lot of guesswork—but not for much longer. With a combination of weather sensors and in-ground moisture monitors, an AI system could intelligently optimize the use of irrigation systems. This would reduce water waste while ensuring each crop receives optimal water for its needs.
Monitoring Plant Health. When an acre could have 100,000+ individual plants, it becomes almost impossible for humans to monitor them all manually—but IoT cameras can. AI can be trained to recognize signs of both disease and pest infestation and send early alerts before the problem has time to spread. This is one of the specialties of Cropler, our own unified visual plant monitoring and reporting system. With Cropler, you can easily monitor all your fields, while results are reported to an easy-to-use dashboard you can access anywhere.
Automated Pesticide Application. Along with monitoring and sending alerts, some AI systems are being deployed in pesticide management. While this field is still being refined, AI promises to be able to identify specific pests when they appear and then apply appropriate amounts of pesticide via AI-controlled drones. This would also reduce the health and ecological problems associated with the overuse of pesticides—another big benefit!
Planting Optimization. Every field is a little different, and there are almost uncountable possible combinations of crops that could be planted in various plots across the seasons. AI has the capability to analyze millions of data points, offering suggestions for the best possible planting combinations for yield or long-term soil health.
Weather Prediction. Weather prediction maynever be a 100% accurate science, but a combination of national weather reports and locally gathered data will allow AI to make good predictions of weather conditions. This can assist with everything from irrigation to planting choices.
Intelligent Harvesting. Smart harvesting devices are already entering use, capable of identifying ripe harvests while passing over immature plants. These harvesting systems can even sort the produce based on size, color, or other specified attributes.
Agricultural Information Management. AI will help in the office as well! The extensive data collection will lead to much smarter decision-making. For example: 
- Plant Breeding: AI can track individual strains, and help select for hardiness, yield, etc. 
- Soil Health: By monitoring soil health and biodiversity, AI can suggest better methods for maintaining the soil. 
- Crop Feeding: Fertilization can be optimized, reducing costs and preventing over-fertilization. 
- Risk Management: From tracking crop prices to predicting weather events, you'll have far more data at your disposal for reducing the risks of bad harvests. 
- Personnel Optimization: How many human workers do you need? Data can help balance workforce vs labor costs.

Challenges Facing AI in Agriculture

As with any new technology, there are some significant challenges worth addressing. AI is almost undoubtedly the future of agriculture, but is now the right time?

Upfront Costs. Right now, costs are the biggest barrier for some operations — especially when combined with a "if it ain't broke, don't fix it" attitude. However, while the full sweep of changes discussed here would be quite costly to implement, starting small can be affordable. For example, individual applications of AI, such as visual monitoring through agri-cameras, are pocket-friendly yet can show their effectiveness relatively quickly.
Rapid Changes. AI is a rapidly developing industry, with new innovations appearing practically every month. It wouldn't be unrealistic to say it's better to wait a few years for the tech to become more stable. In particular, right now it can be a challenge getting various systems from different vendors to "talk to" each other smoothly. Still, avoiding technology and new solutions at all may not be a wise step in the long run. By starting with a few currently available tools, farmers can become ready and more adaptable to future changes and also start to improve their operations and profits now instead of waiting for an ideal moment.
Difficult Adoption. A fully AI-managed farm would be run in a vastly different way than traditional farms. Implementing AI could potentially take years, along with the need to retrain all workers, on top of the labor/installation issues. However, starting to implement these smart-farming solutions step-by-step and exploring their pros and cons with time can be simpler than applying all the new solutions at once later.
Security Issues. Digital systems are always at risk of intrusion, and heavily data-focused farms would become magnets for hackers and other cybercriminals. Robust security systems can add to the overall costs and complexity of adoption.

When you have questions, we'll help you find the answers you need to take control of your fields and the harvest you care about. Contact Cropler to discover more about our products and the ways we help you proactively protect your operations from the effects of crop diseases. Discover More

In Summary: The Future of AI Agriculture

So, combining everything we've talked about, what might a true Farm Of The Future look like?

An AI system that has been installed for years, collecting data.
Smart recommendations for crops, based on past yields, soil health, market conditions, and weather predictions.
Automated tilling and seeding, with density based on optimized projections.
Smart cameras oversee every plant from seed to fruit, sending alerts whenever there are signs of poor plant health, pests, etc.
Automated fertilization, irrigation, and pesticide systems — including drones — kick in as needed to maintain the fields
Smart harvesting systems are triggered when the plants are ready, capable of distinguishing between mature and immature plants, as well as sorting the produce.
All data gets fed back into the central data system to further improve next year's work.

Cropler is helping to make this a reality! Our solar-powered weatherproofed cameras capture images around-the-clock, in both visible and IR spectrums, allowing you to monitor your fields remotely from anywhere while getting hard data on plant health. Ground sensors for moisture and temperature are also coming soon!

To learn more about the Cropler system and how it can leverage agricultural AI to optimize your harvests, just click here to contact us.