000			a
999			_c22746 _d22746
003			OSt
005			20250429110531.0
008			250429b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_926035 _aChakraborty, Uddyalok
245			_aHybrid deep learning with alexnet feature extraction and unet classification for early detection in leaf disease
250			_aVol.14(3), Jan
260			_aChennai _bICT Academy _c2024
300			_a3255-3262p.
520			_aThis study addresses the imperative need for early detection of leaf diseases in tobacco, pepper, and tomato plants, as these diseases significantly impact crop yield and quality. Existing methods often fall short in accurately identifying diseases across diverse plant species. The research aims to bridge this gap by proposing a hybrid deep learning approach, combining the robust feature extraction capabilities of AlexNet with the precise segmentation and classification prowess of UNet. The proposed hybrid model leverages AlexNet proficiency in extracting hierarchical features from plant leaf images and subsequently utilizes UNet for accurate disease classification. This synergistic combination enables the model to overcome the challenges posed by the varied morphologies of tobacco, pepper, and tomato leaves. Experimental results demonstrate the effectiveness of the proposed methodology, showcasing superior performance in terms of accuracy, sensitivity, and specificity compared to existing techniques. The hybrid deep learning approach exhibits promising potential for early and accurate detection of leaf diseases, contributing to sustainable crop management practices.
650		0	_94622 _aComputer Engineering
700			_926036 _aThilagavathy, D.
773	0		_dChennai ICT Academy _tICTACT Journal on Soft Computing (IJSC)
856			_uhttps://ictactjournals.in/paper/IJSC_Vol_14_Iss_3_Paper_4_3255_3262.pdf _yClick here
942			_2ddc _cAR