Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification.

ABSTRACT: Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features dire

SUBMITTER: Khan B 

PROVIDER: S-EPMC11401875 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature