A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection.

ABSTRACT: Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern recognition. However, it has limited interpretability for deep features. With the transfer of expert knowledge, handcrafted features provide a new way for personalized diagnosis of plant diseases. However, irrelevant and redundant features lead to high dimensionality. In this study, we proposed a swarm intelligence algorithm for feature selection [salp swarm algorithm for feature selection (SSAFS)] in image-based plant disease detection. SSAFS is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of features. To verify the effectiveness of the developed

SUBMITTER: Xie X 

PROVIDER: S-EPMC10204742 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature