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Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Models and Transfer Learning Based Approach

Generated Samples

Performance comparison between diffusion models and GANs.

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Fig.1 Performance comparison between diffusion models and state-of-the-art GANs. Best values are in bold.

Diffusion Models

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Fig.2 Real (upper) and generated (lower) weed samples for CottonWeedID15 dataset. Each column represents one weed class.

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Fig.3 Real (upper) and generated (lower) weed samples for DeepWeeds dataset. Each column represents one weed class.

GANs

BigGAN

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Fig.4 Generated weed samples for CottonWeedID15 dataset by BigGAN. Each column represents one weed class.

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Fig.5 Generated weed samples for DeepWeeds dataset by BigGAN. Each column represents one weed class.

StyleGAN2

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Fig.6 Generated weed samples for CottonWeedID15 dataset by StyleGAN2. Each column represents one weed class.

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Fig.7 Generated weed samples for DeepWeeds dataset by StyleGAN2. Each column represents one weed class.

StyleGAN3

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Fig.8 Generated weed samples for CottonWeedID15 dataset by StyleGAN3. Each column represents one weed class.

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Diffusion models in weed recognition

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