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Found 7 papers, 5 papers with code
Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach
In this paper, we present the first work of applying diffusion probabilistic models (also known as diffusion models) to generate high-quality synthetic weed images based on transfer learning.
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review
In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e. g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments.
Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production Systems
Precision weed management offers a promising solution for sustainable cropping systems through the use of chemical-reduced/non-chemical robotic weeding techniques, which apply suitable control tactics to individual weeds.
Out-of-focus Blur: Image De-blurring
Image de-blurring is important in many cases of imaging a real scene or object by a camera.
Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks
The standard LSTM recurrent neural networks while very powerful in long-range dependency sequence applications have highly complex structure and relatively large (adaptive) parameters.
Empirical Evaluation of A New Approach to Simplifying Long Short-term Memory (LSTM)
The standard LSTM, although it succeeds in the modeling long-range dependences, suffers from a highly complex structure that can be simplified through modifications to its gate units.
Food Image Recognition by Using Convolutional Neural Networks (CNNs)
Food image recognition is one of the promising applications of visual object recognition in computer vision.
