Enhancing Flower Classification with Transfer Learning Techniques

In computer vision, identifying flower species from photographs is a difficult task because of various problems such as overfitting, requirement for significant parameter adjustment, limited accuracy, and computational complexity. The InceptionV3 architecture-based environment deep learning model for flower classification used in this study helps to overcome these difficulties. By utilizing numerous convolutional layers, inception modules, and auxiliary classifiers to enhance feature extraction and classification accuracy, the suggested model takes advantage of InceptionV3’s sophisticated capabilities. In order to improve performance, the model in this work underwent extensive data preprocessing and augmentation after being trained on a sizable dataset of flower photos. The design was optimized to minimize computational overhead and prevent overfitting, producing a model that strikes a balance between accuracy and complexity. The InceptionV3-based model’s performance was assessed in a range of scenarios, including variations in training data volumes and input resolutions. The outcomes show that model outperforms a number of cutting-edge techniques for flower classification, achieving a noteworthy accuracy of 99.48% on the test set. This underlines the value of appropriate data pretreatment and augmentation approaches and demonstrates the efficacy of the InceptionV3 architecture in deep learning for picture classification tasks. The results indicate that the InceptionV3 model, which offers high accuracy and enhanced precision, is a reliable and effective method for identifying floral crop.