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Found 10 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.
Fusarium head blight detection, spikelet estimation, and severity assessment in wheat using 3D convolutional neural networks
Moreover, 3D CNN models for FHB severity estimation were created, and our best model achieved 8. 6 MAE.
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.
Plant Species Recognition with Optimized 3D Polynomial Neural Networks and Variably Overlapping Time-Coherent Sliding Window
Recently, the EAGL-I system was developed to rapidly create massive labeled datasets of plants intended to be commonly used by farmers and researchers to create AI-driven solutions in agriculture.
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.
Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography
Our model is implemented as a pipeline consisting of a 2D CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3D CNN that performs a binary classification to detect if the segmented echocardiography shows signs of MI.
1-Dimensional polynomial neural networks for audio signal related problems
We show that this non-linearity enables the model to yield better results with less computational and spatial complexity than a regular 1DCNN on various classification and regression problems related to audio signals, even though it introduces more computational and spatial complexity on a neuronal level.
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.
