Enhancing Flower Identification Using Deep Learning: A Comparative Study Using Multi-Statistical Models

Flower identification is a crucial aspect of plant classification and ecological research, playing a significant role inunderstanding biodiversity and ecosystem dynamics. This research paper presents a new approach to flower identification usingadvanced deep learning techniques. The proposed system used folding networks (CNNs) to automatically extract hierarchicalfeatures from high-resolution images of flowers, allowing for more accurate and efficient classification. The procedure isimplemented as a multi-stage process, beginning with data preprocessing to enhance image quality and remove noise. Usinganother data record, educated CNN models such as modified reset 50, VGG16, or Google are then fine-tuned with commentedflower images. Furthermore, transfer learning is used to properly use knowledge from large data records and improve the abilityof models to generalize different types of flowers.. In the end, our approach achieved an accuracy of 82.04% using VGG16, thehighest compared to other algorithms.