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Unitat de Valorització de la URV posted this:
Licensing Manager at Fundació URV

Automatic feature descriptor in agriculture images using neural networks Software that recognizes user-defined objects in aerial images for agriculture and derives quantitative estimators for plant count (N), green area index (GAI), growth stage (GS), deficient leaf area fraction (DLAF), and weeds area fraction (WAF), and potentially further measures. It employs a pre-trained multi-layer neural network model to classify regions of an image. The network is trained based on good examples of distinct types of objects that are provided by experts in aerial photography for agriculture. Afterwards, any provided aerial image or video can be analyzed with the pretrained software trained once.

AZAM KHAN posted this:
Co-Founder and COO at LexSet.ai

Synthetic data for Computer Vision applications At LexSet, we create fully annotated, bespoke, synthetic datasets that improve Computer Vision accuracy and unlock new use cases. We also offer full stack solutions including trained models and hosted CV systems.. We’re working on teaching AIs the full spectrum of possibilities — and producing superior results. We have achieved classification accuracy up to 99% without the use of real-world data in the training set. Reach out to us today to find out why our clients refer to our results “Pretty Darn Amazing” — Principal Engineer, Major American Robotics Company, and LexSet Client.