Comparative Evaluation of Modified Wasserstein GAN-GP and State-of-the-Art GAN Models for Synthesizing Agricultural Weed Images in RGB and Infrared Domain

Comparative Evaluation of Modified Wasserstein GAN-GP and State-of-the-Art GAN Models for Synthesizing Agricultural Weed Images in RGB and Infrared Domain

This study investigates the application of modified Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic RGB and infrared (IR) datasets to meet the annotation requirements for wild radish (Raphanus raphanistrum). The RafanoSet dataset was used for evaluat...

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Bibliographic Details
Main Authors: Shubham Rana, Matteo Gatti
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:MethodsX
Subjects:
Modified Wasserstein Generative Adversarial Network with Gradient Penalty
Online Access:http://www.sciencedirect.com/science/article/pii/S2215016125001554
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