How do deep learning, computer vision, and hydroponics intersect?

Deep learning, hydroponics, and medical marijuana

In the first half of today’s blog post, we’ll briefly discuss the concept of hydroponic farms, the relation they have to marijuana, and how deep learning intersects them both.

From there we’ll implement a Convolutional Neural Network with Keras to automatically classify root health of plants grown in a hydroponic system without having to physically touch or interfere with the plant.

Finally, we’ll review the results of our experiment.

What is hydroponics?


Hydroponics is a massive industry with an estimated market value of $21,203.5 million USD(yes, million) in 2016. The market is expected to grow at a 6.5% Compound Annual Growth Rate (CAGR) year over year from 2018 to 2023. Europe and Asia are expected to grow at similar rates as well

Hydroponics itself is a subset of hydroculture, the process of growing plants without utilizing soil and instead using mineral-rich solutions in a water solvent.

Using hydroponic methods, plants can grow with only their roots touching the mineral solution.

If you automatically correlate the term “hydroponics” with “marijuana”, keep in mind that hydroponic farming has been endorsed and used by major governments and organizations, including the United States, NASA, Europe and even the fabled Hanging Gardens of Babylon.

A great example is the International Space Station (ISS) — we’ve had hydroponic experiments, including growing vegetables, going on at the ISS for years.

Hydroponics is a science that has existed since the Babylonians and Aztecs, and continues to be used in modern times — so before you turn your nose up, keep in mind that this is actual science, and a science far older than computer vision and deep learning.

What does hydroponics have to do with medical marijuana?

If you read the previous section on what hydroponics is and why we use the method, it should come as no surprise that hydroponics is widely used in the marijuana industry, even before legalization legislation (in some states) in the United States.



The overall goal of the project was to develop an automated root growth analysis system capable of accurately measuring the roots followed by detecting any growth problems:

In particular, roots needed to be classified into two groups:

1. “Hairy” roots
2. “Non-hairy” roots

The “hairier” a root is, the better the root can suck up nutrients.

The “less hairy” the root is, the fewer nutrients it can intake, potentially leading to the plant starving and dying.

For this project, Timothy, along with Mahesh Rangu (a Ph.D. student) and their advisors, Dr. Erdem Erdemir and Dr. Suping Zhou, developed a system to automatically capture root images without having to disturb the plant itself.

Example images of their experimental setup can be seen in Figure 3 at the top of this section.

From there, they needed to apply computer vision to classify the root into one of the two categories (and eventually multiple categories for detecting other root afflictions).

The only question was how to solve the image classification problem?

Our dataset of 1,524 root images includes:

• Hairy: 748 images (left)
• Non-hairy: 776 images (right)

A subset of the example images for each class can be seen in Figure 4 above.

The original images were captured at a higher resolution of 1920×1080 pixels; however, for the sake for this blog post, I’ve resized them to 256×256 pixels as a matter of convenience (and to save space/bandwidth).

The resizing was performed by:

1. Resizing the height to 256 pixels
2. And then taking the center 256-pixel crop

Since the center of the image always contained the mass of root hairs (or lack thereof), this resizing and cropping method worked quite well.

Darrah et al. have graciously allowed us to use these images for our own education as well (but you cannot use them for commercial purposes).

In the remainder , you will learn how to train a deep learning network to automatically classify each of these root species classes.




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Real-Time Root Monitoring of Hydroponic System 
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