Public Lab Research note


Computer Vision/LED Plant Measurement System

by MaggPi | | 566 views | 4 comments |

Read more: publiclab.org/n/15957


The project incorporates a digital camera, light emitting diodes (LEDs), and software image analysis tools to conduct non-contact plant growth analysis. The system measures growth parameters such as leaf size and leaf reflectance. Plant growth characteristics can then be used to regulate the spectral and temporal output of the LED grow lights. The goal of the system is to provide real time feedback to optimize plant development with minimum energy.

I believe this project directly relates to the climate change and food shortage challenges. Combining LED and computer vision technology creates the opportunity to develop plant light algorithms that can simultaneously improve plant yield and greenhouse energy efficiency.

Attached video,https://youtu.be/Y6Vz6sSnXhY, shows computer vision measurements of a basil plant over a 30 day period. The goal was to understand computer vision performance for the LED plant measurement system. Images show the ability of computer vision to measure small features less than 1mm without contact. Computer vision software (SimpleCV) extracts basil plant 'blob' features from soil background and counts number of pixels in each plant 'blob'.

Video info: Unprocessed Image (right), computer vision processed image (left) marks counted pixels in green. Text below displays # of pixels in area/ length/width and perimeter for each image. Images are scaled/calibrated by ruler - 1 pixel measures .6mm x .6mm. blob measurement

Materials for the project are listed here: Materials Project schematic is available at: Schematic

It is also possible to conduct multi spectral observations by sequencing the camera with different LED colors. The objective is to see if reflectance at different wavelengths provide useful information about plant health. Attached figure shows different images ![Mulitspactral4 of the same basil plant illuminated by different LED wavelengths. Top left is a computer vision enhanced image. Top right is white light image. Images below are for ultraviolet (uv1),ultraviolet (uv2), blue(blu), green (grn), red and infrared (ir). Remaining images are processed images that highlight color contrast.

Looking for collaborators to adapt raspberry pi / computer vision tech to conduct real time spatial/spectral analysis. Please respond if you are interested in applying CV techniques to real time grating spectroscopy or multi-spectral imaging systems.

Thank You, MaggPI


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4 Comments

Thanks for posting, @MaggPi, this project looks really exciting! We've linked to it on some of our social media, to give it some attention, but if there are specific questions you're hoping to work on with collaborators you might also consider breaking things up and posting to the questions page. I appreciate how well you've documented the instructions.. I've added an activity tag to help it show up with other projects that might be replicated by folks in the community. Very cool!


Here's an intro the Pytourch, one of the more popular ML/AI frameworks which could be relevant for this project! https://github.com/bayesianio/applied-dl-2018/blob/master/lab-0-SeNet-SeedLings.ipynb


Here is an interesting curveball - I just started using this implementation of rfcnn. It would be really interesting to see if can be used to solve (or help in solving) this problem! Have a look here: https://github.com/fizyr/keras-retinanet


MaggPi very interesting project. Was there a link for status/instructions? I also noticed David Prutchi of Hacketeria had an interesting multi-band polarizing imager, probably we could reach out to him. I have been researching a portable microscopic imager and spectrophotometer. I also thought about how your project could benefit from several VOC/CO2 sensors or light sensors to inform research goals.

Thanks for sharing!

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