• Kondru Paul Prakash
• Venu Gopal Rao Mallipuram
• Ramya Sri Nallagorla
• Anusha Jyothula
• Dr.V.Balaji

We use deep convolutional neural networks to identify the plant species captured in a photograph and evaluate different factors affecting the performance of these networks. We use powerful deep learning architecture, namely MOBILENETV2, are used for this purpose. Transfer learning is used to fine-tune the pre-trained models, using the plant task datasets of MEDICINAL PALNT. To decrease the chance of overfitting, data augmentation techniques are applied based on image transforms such as rotation, translation, reflection, and scaling. Furthermore, the networks parameters are adjusted and different classifiers are fused to improve overall performance. Our best combined system has achieved an overall accuracy of 90%.

Image Identification, Deep Learning, CNN Variants, Medicinal Leaf Dataset

"IDENTIFICATION OF PLANTS USING CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.a522-a529, June-2024, Available :http://www.jetir.org/papers/JETIR2406064.pdf

"IDENTIFICATION OF PLANTS USING CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppa522-a529, June-2024, Available at : http://www.jetir.org/papers/JETIR2406064.pdf