Search Results for author:
Found 6 papers, 1 papers with code
Strategies and impact of learning curve estimation for CNN-based image classification
Learning curves are a measure for how the performance of machine learning models improves given a certain volume of training data.
A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
State-of-the-art methods based on 1D- and 2D-CNNs struggle to efficiently extract spectral and spatial information.
Investigating classification learning curves for automatically generated and labelled plant images
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.
The TerraByte Client: providing access to terabytes of plant data
The user interface is built on top of a low-level client.
Presenting an extensive lab- and field-image dataset of crops and weeds for computer vision tasks in agriculture
These, in total 14, 000 images, had been selected, such that they form a representative sample with respect to plant age and ndividual plants per species.
An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
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.
