Manuscript Number : SHISRRJ247235

Classifying flower images is important for many domains, such as botany, agriculture, and environmental monitoring. This project uses a subset of deep learning called Convolutional Neural Networks (CNNs) to automate the process of categorizing different types of flowers based on their photographs. The suggested method makes use of this to identify and effectively portray complex patterns within the photos by learning hierarchical features straight from raw pixel data. The study's dataset offers a varied assortment of high-resolution photos of flowers from several species. Preprocessing methods are used to improve this model's efficiency, including image resizing and normalization. Convolutional layers for feature extraction, pooling layers for spatial down sampling, and fully connected layers for classification are all part of this architecture. The study investigates transfer learning by employing pre-trained models on extensive image datasets to enhance performance and expedite convergence. Using the Adam optimizer, this model is optimized as part of the training process. Metrics like accuracy are computed to measure the resilience and generalization capacity of the suggested system, and the model's performance is assessed using a different validation set. The results of the trial indicate how effective the this-based system is for classifying floral images, with high accuracy and consistent performance across a range of flower species. The research advances the fields of computer vision and image classification and lays the groundwork for the creation of intelligent systems that will enable automatic identification of flower species in practical applications.