Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Bidinosti, Christopher P"'
Autor:
Didyk, Laura, Yarish, Brayden, Beck, Michael A., Bidinosti, Christopher P., Henry, Christopher J.
Learning curves are a measure for how the performance of machine learning models improves given a certain volume of training data. Over a wide variety of applications and models it was observed that learning curves follow -- to a large extent -- a po
Externí odkaz:
http://arxiv.org/abs/2310.08470
Publikováno v:
Smart Agricultural Technology 5 (2023) 100316
Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It can capture detailed information about the chemical and physical properties of agricul
Externí odkaz:
http://arxiv.org/abs/2306.09418
Autor:
Hamila, Oumaima, Henry, Christopher J., Molina, Oscar I., Bidinosti, Christopher P., Henriquez, Maria Antonia
Fusarium head blight (FHB) is one of the most significant diseases affecting wheat and other small grain cereals worldwide. The development of resistant varieties requires the laborious task of field and greenhouse phenotyping. The applications consi
Externí odkaz:
http://arxiv.org/abs/2303.05634
Publikováno v:
Journal of Magnetic Resonance 345 (2022) 107306
The generation of accurate tip angles is critical for many applications of nuclear magnetic resonance. In low static field, with a linear rather than circular polarized rf field, the rotating-wave approximation may no longer hold and significant devi
Externí odkaz:
http://arxiv.org/abs/2209.03754
In the context of supervised machine learning a learning curve describes how a model's performance on unseen data relates to the amount of samples used to train the model. In this paper we present a dataset of plant images with representatives of cro
Externí odkaz:
http://arxiv.org/abs/2205.10955
In this paper we demonstrate the TerraByte Client, a software to download user-defined plant datasets from a data portal hosted at Compute Canada. To that end the client offers two key functionalities: (1) It allows the user to get an overview on wha
Externí odkaz:
http://arxiv.org/abs/2203.13691
Autor:
Beck, Michael A., Liu, Chen-Yi, Bidinosti, Christopher P., Henry, Christopher J., Godee, Cara M., Ajmani, Manisha
We present two large datasets of labelled plant-images that are suited towards the training of machine learning and computer vision models. The first dataset encompasses as the day of writing over 1.2 million images of indoor-grown crops and weeds co
Externí odkaz:
http://arxiv.org/abs/2108.05789
Autor:
Beck, Michael A., Liu, Chen-Yi, Bidinosti, Christopher P., Henry, Christopher J., Godee, Cara M., Ajmani, Manisha
A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such a
Externí odkaz:
http://arxiv.org/abs/2006.01228
In pTx MRI systems, the prediction of local SAR is based on numerical electromagnetic (EM) simulations and used to scale RF power to ensure FDA SAR limits are not exceeded. This prediction becomes more complex when superposition of E-fields from mult
Externí odkaz:
http://arxiv.org/abs/1910.08047
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