Automated Flower Species Recognition Using Deep Learning

Abstract

This research examines the capability of Convolutional Neural Networks (CNNs) to automatically detect plant species from images of flowers. Flowers are well-suited for species identification because they have a unique shape, color, and structural pattern that tends to be constant even when exposed to different environmental conditions like changes in weather or aging of plants.Traditional recognition approaches usually depend on manually designed features, which may miss important natural cues and tend to need expert domain knowledge. By contrast, CNNs learn and extract deep feature representations automatically from image data, detecting subtle visual details that are crucial for correct classification.In this paper, a model based on CNN is trained and fine-tuned for flower species classification. The model was tested over a benchmark dataset of 967 flower species, and the classification accuracy at rank-1 and rank-10 was 67.45% and 90.82%, respectively. These performances dramatically surpass conventional methods such as Kernel Descriptor (KDES), with as much as six times higher accuracy.The report presents CNNs as a robust and effective tool for designing next-generation image-based plant identification systems with potential applications in botanical research, tracking biodiversity, and digital information retrieval