How Artificial Intelligence Can Help in Agriculture

While it seems obvious how artificial intelligence can be applied in other sectors of the economy, like finance and medicine, it doesn’t immediately become clear how AI can fit well in any agricultural process.

A farmer may actually wonder how AI can actually work the physical land. 

And this mostly stems from the fact that any machine learning project worth its salt relies on data, and most agricultural processes are not quite set for data collection yet.

However, that couldn’t be further from the truth. In fact, a report by Emergen Research established that the AI in agriculture market size reached $USD 1.7 billion in 2021.

Even though most agricultural processes and stages are manual, artificial intelligence technologies can still help facilitate the most complex and routine tasks by collecting and analyzing large amounts of data in order to come up with the best course of action.

So in this article, we are going to look at some of the most practical applications of artificial intelligence and machine learning in agriculture. 

Let's get started.

1. Crop and Soil Monitoring

Manual crop and soil monitoring can quickly become costly.

It is for the very reason that checking soil moisture manually will increase labor costs as well as operating costs.

In addition to that, the manual nature of soil inspection will often result in inaccurate data because it’s always hard to collect real-time data for the whole farm for processing and analysis.

If you don’t have real-time data, for example, data on the soil moisture, the crops will most likely either have too much or too little water. Too much water leads to both wastages as well as damage to the crops.

Artificial intelligence can help with soil and crop monitoring.

Through the use of sensors in the field and some well-trained machine learning algorithms, we can collect and analyze data on various parameters like moisture levels, soil nutrient levels, and temperature.

Data collection and analysis are definitely inevitable in any digital transformation initiative, as Amanda Jerelyn explains here… and the agriculture sector must now integrate data collection procedures in order to obtain quality data for deep learning algorithms.

Once we analyze this data through a machine learning algorithm, we can get insights into the most optimal conditions for crop growth and soil health. We’ll also be able to discover certain trends and patterns that are key to understanding soil conditions.

With this information, a farmer will be able to take appropriate measures to improve farm soil health, thereby improving productivity.

2. Livestock Health Monitoring

In the traditional sense, livestock health monitoring involves regularly observing and inspecting the animals by trained personnel.

It would begin by first checking the animal’s behavior and physical appearance. Subsequent steps involve taking their temperature, listening to their heartbeat, and then checking for any obvious signs of illness or injury.

From the results of these observations, the personnel may identify potential health risks and then take appropriate action.

While these personnel may be well trained, these tests are time-consuming and may not be as accurate. Besides, it’ll be really hard to scale when working on a farm with a large number of animals.

AI comes in with more accurate and scalable approaches.

Leveraging artificial intelligence for livestock health monitoring involves installing sensors and cameras in the animals’ living places.

This enables you to collect useful data on key aspects like the animal’s body temperature, behavior, and heartbeat. You can then feed this data into machine learning algorithms that will analyze it and detect potential signs of illness.

Once a potential health issue is detected, it sends an alert to the farmer or a veterinarian for a human investigation and intervention.

What’s more, the AI provides these alerts in real-time, thereby allowing for early detection and intervention on potential animal health issues.

3. Automatic Weeding

Yes, weeds.

One of the biggest threats to food security emanates from low agricultural yields, and weed infestations are at the center of this threat.

In fact, the WSSA weed loss committee reported a loss of up to 60% in corn yields in North America when no form of weed control is employed at all.

Manually weeding is a very labor-intensive activity. It’s for this reason that the main solution for a long time has been to find chemicals (herbicides) that can selectively kill the weeds while keeping the crops.

First of all, this adds to the cost as herbicides are quite costly, and secondly, it contributes to pollution and crop contamination.

Artificial intelligence can collaborate in weed prevention through robots.

Through AI, you can develop an autonomous robot that can distinguish weeds from crops. It’s able to do this even at the very early sprouting stages when seedlings and weeds may actually look similar.

Armed with computer vision technology, the AI-powered robot then moves through the field, looking at the soil in search of weeds. 

Once it detects a weed, it kills it immediately by zapping it with a laser beam.

Since it’s automated and motor-powered it can work much faster than a human, being more accurate while cutting down on costs and herbicide risks.

4. Produce Grading & Sorting

How important is product grading and sorting?

Sorting and grading fruits and vegetables is a crucial part of any agricultural production process. It helps identify foreign materials while also eliminating fruits that do not meet quality assurance standards.

But it’s also at this point where the most food loss and wastage occurs.

In fact, an inefficient grading and sorting process may lead to a 30% wastage in fruits and vegetables, according to a recent report.

This stems from the fact that most food sorting today is still done manually, which leaves a lot of room for human error. You may fail to notice a foreign object or spoiled product, with adverse repercussions.

However, an AI-powered sorting machine can be the magic wand.

Computer vision is not reserved for high-end solutions. Instead, it is one of the key artificial intelligence applications in agriculture that make machine sorting of fruits and vegetables possible. 

By combining machine learning algorithms and cameras, a machine can visually inspect a range of objects and detect flaws, contaminations, as well as product defects.

Initially, it’ll need to be fed with a ton of data on images of healthy and unhealthy fruits and vegetables. This way, it’ll be able to learn from the provided samples, differentiate spoiled from healthy vegetables, and classify them according to shape, size, color, and quality.

The result is a faster, more reliable, and more consistent sorting machine.

5. Intelligent Spraying

There’s a worldwide increase in food demand.

This has primarily been propelled by the exponential growth in the human population in recent decades. In fact, the United Nations estimates that 9.8 billion people will need food in 2050.

So there’s an urgent need to increase food production, something that has led to an increase in the exploitation of vital resources like water and fertilizers.

Achieving these results should be done without compromising the key physical and chemical components of soil. It’s the only surefire way to a sustainable food production model.

So how does artificial intelligence come to the rescue?

Precision farming is transforming the agricultural world.

Precision farming refers to a collection of strategies and tools that enables a farmer to optimize soil quality and productivity through a series of targeted interventions built on advanced technologies.

Thanks to technological advances in computer vision, mechatronics, and machine learning, we can now develop and implement remote sensing technologies to aid with weed, pest, and disease management.

Armed with deep learning and IoT technologies, farmers and the agtech industry can now reimagine the future of agriculture.

Conclusion

The future of artificial intelligence in agriculture is in sight.

Recent technological advancements in computer vision, deep learning and the IoT have created a lot of excitement around the capabilities of AI in agriculture.

There are still a ton of challenges to be overcome in these applications of artificial intelligence in agriculture.

When conducting agricultural data analysis, there’s the problem of the availability of the data in the first place. This is followed by the quality of the available data to make it suitable for analysis.

Then how affordable are the artificial intelligence platforms to develop on?

Besides, there are also raging debates on the potential risks of incorporating artificial intelligence in large-scale agricultural operations.

On the flip side, AI has been successfully implemented in other aspects of agriculture, like soil monitoring, livestock health monitoring, and automatic weeding. Automated grading and sorting have also been met with a great deal of success.

I’m closely following how farmers are adapting to these advancements.

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