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This paper discusses the use of Deep Convolutional Generative Adversarial Network (DCGAN) to generate synthetic datasets for cotton leaf images to improve the training of machine learning models for disease detection. The authors highlight the challenges of obtaining balanced datasets in real-world applications and demonstrate that DCGAN can effectively augment existing datasets, resulting in improved accuracy for disease detection algorithms. The methodology and results indicate that synthetic datasets gen

Author

pujiitasrieyabolisetty

Language

EN