8 Practical Applications of AI in Agriculture

Learn how V7 can help you build AI solutions for your use case in the agriculture or farming sector.

Crop and soil monitoring

Let’s start from the ground up.

Micro and macronutrients in the soil are critical factors for crop health and both the quantity and quality of yield.

Then, once crops are in the soil, monitoring the stages of growth is also essential to optimizing production efficiency. It’s vital to understand interactions between crop growth and the environment in order to make adjustments for improved crop health.

Now, traditionally soil quality and crop health were determined by human observation and judgment. But this method is neither accurate nor timely.

Instead, we can now use drones (UAVs) to capture aerial image data, and train computer vision models to use this for intelligent monitoring of crop and soil conditions.

Visual sensing AI can analyze and interpret this data to:

• track crop health

• make accurate yield predictions.

• detect crop malnutrition much faster than humans

AI models can inform farmers of specific problem areas so that they can take immediate action. 

Now let’s look at some real examples of how computer vision is helping keep their crops healthy and productive.  

Don't forget to also check out 27+ Most Popular Computer Vision Applications and Use Cases.

Observing crop maturity

Manual observation of wheat head growth stages is just the kind of labor-intensive process that AI can help with in precision agriculture.

Researchers achieved this by collecting images of wheat at different “heading” stages across three years and in different lightings, which enabled them to create a “two-step coarse-to-fine wheat ear detection mechanism”.

This computer vision model was then able to outperform human observation in accurately identifying wheat growth stages, meaning that the farmers no longer had to make daily treks into the fields to examine their crop.

Or imagine having to check the ripeness of tomatoes by hand on an industrial level. Well.. 

AI can help with that, too!

Another study examined how well computer vision can detect maturity in tomatoes. Researchers created an algorithm that analyzed color from five different parts of the tomato, and then made maturity estimates based on this data.

How did it do?

Shockingly well! The algorithm achieved a successful detection and classification rate of 99.31%.

Overserving and estimating crop growth and maturity is hard, labor-intensive work for farmers. But AI is proving capable of handling much of that work with both ease and impressive accuracy.

Looking for the perfect bounding box tool? Check out 9 Essential Features for a Bounding Box Annotation Tool.

Hitting the Ground with Computer Vision

Getting back to the importance of soil, another study set out to see how well computer vision can characterize soil texture and soil organic matter (SOM).

Ordinarily, evaluating soil requires farmers to dig up samples and bring them to a lab for time- and energy-intensive analysis. Instead, researchers decided to see if image data from an inexpensive handheld microscope could be used to train an algorithm to do the same thing.

Sure enough, the computer vision model managed to make sand content and SOM estimates with accuracy comparable to costly lab processing. 

So, not only can computer vision eliminate a large amount of the difficult, manual labor involved in crop and soil monitoring, in many cases it does it more effectively than humans can.

Modern AI applications for agricultural practices go beyond computer vision, AI data extraction tools can help speed up and scale processing and organizational tasks.

Insect and plant disease detection

We’ve seen how AI computer vision can detect and analyze crop maturity and soil quality, but what about agricultural conditions that are less predictable?

Using image recognition technology based on deep learning, we can now automate detection of plant diseases and pests. This works using image classification, detection, and image segmentation methods to build models that can “keep an eye” on plant health.

Take a look at how this works:

Keeping out the bad apples (diagnosing disease severity) 

A good example of this in action comes from a study of apple black rot (which we’d all probably prefer off our apples!).

Researchers trained a Deep Convolutional Neural Network using images of apple black rot which had been annotated by botanists according to four major stages of severity.

As with our previous examples, the alternative to computer vision requires a lot of labor-intensive human searching and evaluation. Fortunately for farmers, the AI model in this study was able to identify and diagnose disease severity with an accuracy of 90.4%!

Researchers in another study went even further by using an improved YOLO v3 algorithm to detect multiple diseases and pests on tomato plants.

Armed with a digital camera and a smartphone, the researchers took photos at local tomato greenhouses and identified 12 different cases of either disease or pests.

Once the model was trained using the images, which varied in resolution and size of the object featured, it achieved a disease and pest detection accuracy of 92.39% with a detection time of just 20.39 ms.

Not too shabby!

Looking for quality training data? Check out 65+ Best Free Datasets for Machine Learning.

Finding bugs with code

And say you’d like to know not only if your crops have pests, but how many there are, computer vision systems for insect detection has that covered as well.

Did we mention that this works for flying insects, too? 

They’re certainly not the most fun to capture and count by hand.

Researchers first set up a sticky trap to capture six different species of flying insect and collect real-time images. They then based the detection and coarse counting method on YOLO object detection, and the classification and fine counting on Support Vector Machines (SVM) using global features.

When all was said and done, their computer vision model was able to identify bees, flies, mosquitoes, moths, chafers, and fruit flies with an accuracy of 90.18%, and count them with 92.5% accuracy.

These studies show that the future of AI computer vision for monitoring the health of our food systems is promising. Not only can it reduce labor inefficiencies, but it can do so without sacrificing reliability of the observations.

Livestock health monitoring

So far we’ve focused mainly on plants, but there’s more to agriculture than wheat, tomatoes, and apples.

Animals are another major component of our agriculture systems, and they tend to need a bit more tracking than plants. Can computer vision keep up with cows, chickens, and pigs on the move?

Well, if it can track a fly, it can certainly track a cow.

Check out below how one of V7's users—CattleEye’s training data allows for tracking and annotating of cattle using bounding boxes and key points.

CattleEye is a great example of an AI-first company in the agriculture industry. They use overhead cameras and computer vision algorithms to monitor cattle health and behavior.

This means that spotting a problem isn’t dependent on a cattle farmer being right there next to the cow. Instead, the cattle can be tracked and monitored remotely and in real-time so that farmers can be notified as soon as a problem is observed.

Of course, this isn’t just limited to cattle. Computer vision can also:

• Count animals, detect disease, identify unusual behavior, and monitor significant activities such as giving birth.

• Collect data from cameras and drones (UAVs).

• Combine with other technologies to keep farmers informed on animal health and access to food or water.

Plus, take a look at how this image of chickens has been annotated with V7.

The algorithms are trained to look at video data and determine what the chickens are up to – whether they’re drinking, eating, sleeping, or doing something odd that may be indicative of disease or behavioral problems.

Farmers can also use OCR software to track cattle tags and organise manual paperwork tasks.