id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
easat-2372	Sharma, Pooja ; Khunteta, Ajay 	Improving losses & accuracy through design of deep convolutional generative adversarial network (DCGAN) for plant disease detection tasks	2024	10	.pdf	application/pdf	4574	277	46	The CNN model trained with DCGAN images outperforms the baseline in almost every metric. In this paper, we address this limitation by employing advanced data augmentation techniques, including Deep Convolutional Generative Adversarial Networks (DCGANs), to generate synthetic images that expand the dataset of rice leaf diseases.	cache/easat-2372.pdf	txt/easat-2372.txt