• Ms. S. Soniya Reddy
• Dr M. Madhusudhana Reddy

The automatic classification and identification of various plant leaf species has become a popular practice among academics and scholars. They create a model using a variety of deep learning approaches and strategies in order to get a result with more precision. Scientists are increasingly using convolutional neural networks to categorize plant leaves. With increasingly rare species and complex backdrops, classification of plant leaves can be difficult, thus researchers used many models to attain high levels of accuracy. We developed a model for plant leaf classification in the current work for the classification of leaves based on a dataset we gathered. We employed the Resnet-50 model, a well-known CNN architecture, which offered an effective way to organize and analyze a deep classification in order to reduce the complexity and make training use fewer parameters and take less time. We wanted to create a noteworthy result for our classification model using Resnet-50. The influence of the convolutional neural network in feature extraction and classification is well known. We were able to train deep networks in our model thanks to Resnet-50, a residual network. While the average testing accuracy was 92.5%, the average training accuracy was 98.3%. Effective accuracy and the fact that we trained the model using our own prepared dataset, which we created from real-world data, are the study's two main contributions.

Pneumonia, Chest X-ray images. Deep Learning, CNN, CovXNet, RNN, VGG16

" Classification of Plant Species from Images Using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.g176-g179, July-2023, Available :http://www.jetir.org/papers/JETIR2307627.pdf

" Classification of Plant Species from Images Using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppg176-g179, July-2023, Available at : http://www.jetir.org/papers/JETIR2307627.pdf