An embedded system for the automated generation of labeled plant images
to enable machine learning applications in agriculture

An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture

1 June 2020

Michael A. Beck

Papers citing "An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture"

9 / 9 papers shown

Title
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 C. Bidinosti Christopher J. Henry 13 19 0 15 Jun 2023
Inside Out: Transforming Images of Lab-Grown Plants for Machine Learning Applications in Agriculture Alexander E. Krosney Parsa Sotoodeh Christopher J. Henry Michael A. Beck C. Bidinosti 21 3 0 05 Nov 2022
Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models Amogh Joshi Dario Guevara Mason Earles 13 10 0 04 Aug 2022
A Case for Dataset Specific Profiling Seth Ockerman John Wu Christopher Stewart 16 0 0 01 Aug 2022
Investigating classification learning curves for automatically generated and labelled plant images Michael A. Beck C. Bidinosti Christopher J. Henry Manisha Ajmani 12 0 0 22 May 2022
The TerraByte Client: providing access to terabytes of plant data Michael A. Beck C. Bidinosti Christopher J. Henry Manisha Ajmani 17 3 0 25 Mar 2022
Plant Species Recognition with Optimized 3D Polynomial Neural Networks and Variably Overlapping Time-Coherent Sliding Window Habib Ben Abdallah C. Henry S. Ramanna 36 2 0 04 Mar 2022
Presenting an extensive lab- and field-image dataset of crops and weeds for computer vision tasks in agriculture Michael A. Beck Chen-Yi Liu C. Bidinosti C. Henry Cara M. Godee Manisha Ajmani 3DV VLM 26 5 0 12 Aug 2021
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Z. Tu Kaiming He 297 10,220 0 16 Nov 2016