3 citations · 3 across the 3 of their papers we have counts for
4 papers · 1 filter
Uncertainty Guarantees on Automated Precision Weeding using Conformal Prediction
Paul Melki, Lionel Bombrun, Boubacar Diallo +2
Precision agriculture in general, and precision weeding in particular, have greatly benefited from the major advancements in deep learning and computer vision. A large variety of c…
The Penalized Inverse Probability Measure for Conformal Classification
Paul Melki, Lionel Bombrun, Boubacar Diallo +2
The deployment of safe and trustworthy machine learning systems, and particularly complex black box neural networks, in real-world applications requires reliable and certified guar…
Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation
Bart M. van Marrewijk, Charbel Dandjinou, Dan Jeric Arcega Rustia +5
Optimizing deep learning models requires large amounts of annotated images, a process that is both time-intensive and costly. Especially for semantic segmentation models in which e…
Group-Conditional Conformal Prediction via Quantile Regression Calibration for Crop and Weed Classification
Paul Melki, Lionel Bombrun, Boubacar Diallo +2
As deep learning predictive models become an integral part of a large spectrum of precision agricultural systems, a barrier to the adoption of such automated solutions is the lack…