This 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.