Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification.

ABSTRACT: Tomato cultivation is expanding rapidly, but the tomato sector faces significant challenges from various sources, including environmental (abiotic stress) and biological (biotic stress or disease) threats, which adversely impact the crop's growth, reproduction, and overall yield potential. The objective of this work is to build deep learning based lightweight convolutional neural network (CNN) architecture for the real-time classification of biotic stress in tomato plant leaves. This model proposes to address the drawbacks of conventional CNNs, which are resource-intensive and time-consuming, by using optimization methods that reduce processing complexity and enhance classification accuracy. Traditional plant disease classification methods predominantly utilize CNN based deep learning tech

SUBMITTER: Amudha M 

PROVIDER: S-EPMC11620619 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature