40 citations · 93 across the 9 of their papers we have counts for
9 papers
Generative diffusion models for agricultural AI: plant image generation, indoor-to-outdoor translation, and expert preference alignment
Da Tan, Michael Beck, Christopher P. Bidinosti +2
The success of agricultural artificial intelligence depends heavily on large, diverse, and high-quality plant image datasets, yet collecting such data in real field conditions is c…
A Low-Cost Photogrammetry System for 3D Plant Modeling and Phenotyping
Joe Hrzich, Michael A. Beck, Christopher P. Bidinosti +3
We present an open-source, low-cost photogrammetry system for 3D plant modeling and phenotyping. The system uses a structure-from-motion approach to reconstruct 3D representations…
Strategies and impact of learning curve estimation for CNN-based image classification
Laura Didyk, Brayden Yarish, Michael A. Beck +2
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…
A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
Nooshin Noshiri, Michael A. Beck, Christopher P. Bidinosti +1
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 deta…
Inside Out: Transforming Images of Lab-Grown Plants for Machine Learning Applications in Agriculture
A. E. Krosney, P. Sotoodeh, C. J. Henry +2
Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset…
Investigating classification learning curves for automatically generated and labelled plant images
Michael A. Beck, Christopher P. Bidinosti, Christopher J. Henry +1
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…
The TerraByte Client: providing access to terabytes of plant data
Michael A. Beck, Christopher P. Bidinosti, Christopher J. Henry +1
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…
Presenting an extensive lab- and field-image dataset of crops and weeds for computer vision tasks in agriculture
Michael A. Beck, Chen-Yi Liu, Christopher P. Bidinosti +3
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…
An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
Michael A. Beck, Chen-Yi Liu, Christopher P. Bidinosti +3
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 agric…