Machine Learning in Agriculture

Doing the Impossible: As Simple as Drawing Shapes

We’ve all heard tech companies discuss implementing machine learning into their products, but what does that mean? Essentially, it’s training a computer to detect patterns by recognizing similarities between different data layers.


It sounds simple until you realize the sheer magnitude of data needed to generate useful and consistent results, which is why companies like Google and Facebook are pioneers of Machine Learning. With millions of users around the globe, they have no shortage of data to improve their algorithms.

Caution: Slow-Moving Research

So why has the data-driven revolution taken so long to apply in agriculture? The difficulty lies in the complexity of patterns in the fields. One patch of weeds can look completely different from another, so how can a computer be expected to pick up any discernible pattern?


That’s where the human element comes in. In many machine learning scenarios, the human role is simple enough that the work can be outsourced to anyone. For example, a company trying to improve a self-driving car can have people filter through images and ask “Do you see a stop sign in this picture?” which is easy since everyone is familiar with stop signs. The results are compared with the computer’s until it has enough data to identify them as well as we do.

Accomplishing the Impossible

The problem with agricultural patterns is they aren’t as easily recognized. While the average joe has no issues finding a stop sign in an image of a street, they’ll likely struggle to identify an area of nitrogen-deficient corn. The burden, then, lies on those with an agricultural background; however, what agronomist wants to spend time filtering through image after image?


Intelinair employs a team of specially trained annotation specialists who identify and annotate patterns consistent with agricultural issues. This information is then fed to our Machine Learning team, arming them not just with data, but also an understanding of the management practices that make their work critical for the success of AI in agriculture.

Building Ag ImageNet

In precision agriculture and machine learning, accuracy counts. To the casual observer, it may look like our analysts are drawing shapes over pictures of fields, but we’re taking part in the process of multi-spectral validation to build an Ag ImageNet. With this, IntelinAir is poised to be a pioneer in digital agriculture as we incorporate machine learning into AgMRI.

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