Enot, a developer of neural network optimization tools, has partnered with Weedbot. Weedbot’s Lumina, which uses laser, can process imagery faster and with more accuracy thanks to Enot’s neural network optimization technology.
Lumina is a laser weeding implement developed by Weedbot. It has a modular design, with one module taking care of one ridge of the crop. Each laser weeding module contains all necessary components to operate fully independently – lasers, cameras and an embedded GPU processing unit.
A camera captures an image from the top of the ridge, whereupon a neural network based algorithm finds all plants in the image and separates weeds from the crop seedlings. As a final step, a laser beam is applied to the weeds so the water in the leaves boils up. After the treatment, the weed’s leaves wilt.
The baseline segmentation model uses high resolution RGB images to be able to detect the smallest parts of the plants.
Weedbot and Enot set out to speed up and improve the model. Enot’s framework enabled Weedbot to find the optimal operations, neural network depth and width, as well as input resolution that meets the required speed of operation at the best achievable accuracy.
According to both companies, integration of Enot’s neural network optimization technology in Weedbot’s laser weeding machinery, increased the image processing speed by 2.72 times and achieved a 25% accuracy improvement.
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Enot developed two different AI framework versions, with the following goals:
By integrating Enot’s framework, Weedbot was able to achieve the following: