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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Elsevier
2025-06-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125001554 |
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| author | Shubham Rana Matteo Gatti |
| author_facet | Shubham Rana Matteo Gatti |
| author_sort | Shubham Rana |
| collection | DOAJ |
| description | 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 evaluation. Traditional WGAN models struggle with vanishing gradients and poor convergence, affecting data quality. Customizations in WGAN-GP improved synthetic image quality, especially in maintaining SSIM for RGB datasets. However, generating high-quality IR images remains challenging due to spectral complexities, with lower SSIM scores. Architectural enhancements including transposed convolutions, dropout, and selective batch normalization improved SSIM scores from 0.5364 to 0.6615 for RGB and from 0.3306 to 0.4154 for IR images. This study highlights the customized model's key features: • Produces a 128 × 7 × 7 tensor, optimizes feature map size for subsequent layers, with two layers using 4 × 4 kernels and 128 and 64 filters for upsampling. • Uses 3 × 3 kernels in all convolutional layers to capture fine-grained spatial features, incorporates batch normalization for training stability, and applies dropout to reduce overfitting and improve generalization. |
| format | Article |
| id | doaj-art-9efe970e522045fd8fb401a23e2d2b43 |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | MethodsX |
| spelling | doaj-art-9efe970e522045fd8fb401a23e2d2b432025-08-20T03:24:48ZengElsevierMethodsX2215-01612025-06-011410330910.1016/j.mex.2025.103309Comparative Evaluation of Modified Wasserstein GAN-GP and State-of-the-Art GAN Models for Synthesizing Agricultural Weed Images in RGB and Infrared DomainShubham Rana0Matteo Gatti1Corresponding author.; Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, Piacenza, 29122, ItalyDepartment of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, Piacenza, 29122, ItalyThis 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 evaluation. Traditional WGAN models struggle with vanishing gradients and poor convergence, affecting data quality. Customizations in WGAN-GP improved synthetic image quality, especially in maintaining SSIM for RGB datasets. However, generating high-quality IR images remains challenging due to spectral complexities, with lower SSIM scores. Architectural enhancements including transposed convolutions, dropout, and selective batch normalization improved SSIM scores from 0.5364 to 0.6615 for RGB and from 0.3306 to 0.4154 for IR images. This study highlights the customized model's key features: • Produces a 128 × 7 × 7 tensor, optimizes feature map size for subsequent layers, with two layers using 4 × 4 kernels and 128 and 64 filters for upsampling. • Uses 3 × 3 kernels in all convolutional layers to capture fine-grained spatial features, incorporates batch normalization for training stability, and applies dropout to reduce overfitting and improve generalization.http://www.sciencedirect.com/science/article/pii/S2215016125001554Modified Wasserstein Generative Adversarial Network with Gradient Penalty |
| spellingShingle | Shubham Rana Matteo Gatti Comparative Evaluation of Modified Wasserstein GAN-GP and State-of-the-Art GAN Models for Synthesizing Agricultural Weed Images in RGB and Infrared Domain MethodsX Modified Wasserstein Generative Adversarial Network with Gradient Penalty |
| title | Comparative Evaluation of Modified Wasserstein GAN-GP and State-of-the-Art GAN Models for Synthesizing Agricultural Weed Images in RGB and Infrared Domain |
| title_full | Comparative Evaluation of Modified Wasserstein GAN-GP and State-of-the-Art GAN Models for Synthesizing Agricultural Weed Images in RGB and Infrared Domain |
| title_fullStr | Comparative Evaluation of Modified Wasserstein GAN-GP and State-of-the-Art GAN Models for Synthesizing Agricultural Weed Images in RGB and Infrared Domain |
| title_full_unstemmed | Comparative Evaluation of Modified Wasserstein GAN-GP and State-of-the-Art GAN Models for Synthesizing Agricultural Weed Images in RGB and Infrared Domain |
| title_short | Comparative Evaluation of Modified Wasserstein GAN-GP and State-of-the-Art GAN Models for Synthesizing Agricultural Weed Images in RGB and Infrared Domain |
| title_sort | comparative evaluation of modified wasserstein gan gp and state of the art gan models for synthesizing agricultural weed images in rgb and infrared domain |
| topic | Modified Wasserstein Generative Adversarial Network with Gradient Penalty |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125001554 |
| work_keys_str_mv | AT shubhamrana comparativeevaluationofmodifiedwassersteingangpandstateoftheartganmodelsforsynthesizingagriculturalweedimagesinrgbandinfrareddomain AT matteogatti comparativeevaluationofmodifiedwassersteingangpandstateoftheartganmodelsforsynthesizingagriculturalweedimagesinrgbandinfrareddomain |