Advances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future research directions

P Alimisis, I Mademlis, P Radoglou-Grammatikis… - arXiv preprint arXiv …, 2024 - arxiv.org
Image data augmentation constitutes a critical methodology in modern computer vision
tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; …

Coal type identification with application result quantification based on deep-ensemble learning and image-encoded reflectance spectroscopy

Z Yan, D Xiao, H Sun, L Zhang, L Yin - Fuel, 2024 - Elsevier
Accurate coal type identification is essential for efficient coal utilization. This study proposes
a coal type identification method based on image-encoded reflectance spectra and deep …

Bridging real and simulated data for cross-spatial-resolution vegetation segmentation with application to rice crops

Y Gao, L Li, M Weiss, W Guo, M Shi, H Lu… - ISPRS Journal of …, 2024 - Elsevier
Accurate image segmentation is essential for image-based estimation of vegetation canopy
traits, as it minimizes background interference. However, existing segmentation models …

Synthesizing training data for intelligent weed control systems using generative ai

S Modak, A Stein - International Conference on Architecture of Computing …, 2024 - Springer
Deep Learning already plays a pivotal role in technical systems performing various crop
protection tasks, including weed detection, disease diagnosis, and pest monitoring …

An attempt to generate panoramic radiographs including jaw cysts using StyleGAN3

M Fukuda, S Kotaki, M Nozawa, K Tsuji… - Dentomaxillofacial …, 2024 - academic.oup.com
Objectives The purpose of this study was to generate radiographs including dentigerous
cysts by applying the latest generative adversarial network (GAN; StyleGAN3) to panoramic …

Low-Cost Training of Image-to-Image Diffusion Models with Incremental Learning and Task/Domain Adaptation

H Antona, B Otero, R Tous - Electronics, 2024 - mdpi.com
Diffusion models specialized in image-to-image translation tasks, like inpainting and
colorization, have outperformed the state of the art, yet their computational requirements are …

Few-shot Metric Domain Adaptation: Practical Learning Strategies for an Automated Plant Disease Diagnosis

S Kudo, S Kagiwada, H Iyatomi - arXiv preprint arXiv:2412.18859, 2024 - arxiv.org
Numerous studies have explored image-based automated systems for plant disease
diagnosis, demonstrating impressive diagnostic capabilities. However, recent large-scale …

Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems

S Modak, A Stein - arXiv preprint arXiv:2411.00548, 2024 - arxiv.org
In automated crop protection tasks such as weed control, disease diagnosis, and pest
monitoring, deep learning has demonstrated significant potential. However, these advanced …

Synthesizing Training Data for Intelligent Weed Control Systems Using

AI Generative, S Modak, A Stein - Architecture of Computing …, 2024 - books.google.com
Deep Learning already plays a pivotal role in technical sys-tems performing various crop
protection tasks, including weed detec-tion, disease diagnosis, and pest monitoring …

[HTML][HTML] Plant Disease Detection Leveraging Latent Space based Mixing Methods for Image Data Augmentation

VA Suryawanshi, TK Sarode, SA Adivarekar - Plant Disease - agriculturejournal.org
Plant Disease Detection (PDD) is a crucial task in the field of agriculture since it directly
affects plant production and subsequently the economy, social structure, and political …