Utilizing CNN Architectures for Herbal Leaf Health and Pathogen Detection in Deep Learning

Authors- Sripriya M S, Assistant Professor Dr. Nandhini. K

Abstract-– This research delves into the transformative application of Convolution Neural Networks (CNNs) in the realm of herbal plant identification and categorization. CNNs, a subset of artificial neural networks, have emerged as a powerful tool for processing visual data, particularly in the context of image-based tasks such as recognizing diseases in herbal plants. The architecture of CNNs, characterized by convolution layers for hierarchical feature extraction, proves instrumental in capturing intricate patterns within images. Through meticulous data preprocessing and augmentation techniques, including normalization and data augmentation, the model is optimized for robust performance. Training CNNs involves exposing the model to labeled datasets, with transfer learning from pre-trained models facilitating accelerated knowledge transfer. The application of CNNs in herbal plant identification showcases notable advancements in accuracy and speed compared to traditional methods. This research envisions the continued refinement of CNN-based models, addressing challenges such as limited annotated datasets and interpretability concerns. As we look forward, the integration of CNNs into agricultural practices holds the promise of revolutionizing disease detection in herbal plants, contributing significantly to global food security by enabling early and accurate identification of plants.

DOI: /10.61463/ijset.vol.13.issue2.399