LeafDNet: Transforming Leaf Disease Diagnosis Through Deep Transfer Learning.

ABSTRACT: The health and productivity of plants, particularly those in agricultural and horticultural industries, are significantly affected by timely and accurate disease detection. Traditional manual inspection methods are labor-intensive, subjective, and often inaccurate, failing to meet the precision required by modern agricultural practices. This research introduces an innovative deep transfer learning method utilizing an advanced version of the Xception architecture, specifically designed for identifying plant diseases in roses, mangoes, and tomatoes. The proposed model introduces additional convolutional layers following the base Xception architecture, combined with multiple trainable dense layers, incorporating advanced regularization and dropout techniques to optimize feature extraction and

SUBMITTER: Sultan T 

PROVIDER: S-EPMC11815709 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature