Enhanced Flower Classification Using Convolutional Neural Networks: A Comparative Study of Bellflower, Common Daisy, Rose, and Sunflower Identification

Flower classification is vital in many disciplines, including botany, agriculture, environmental studies, and industry. Dependent traditionally on professional knowledge, improvements in digital image processing and machine learning techniques have changed this activity. This paper investigates the use of Convolutional Neural Networks (CNNs) for four classifications bellflower, common daisy, rose, and sunflower—of flower images. Based on a Kaggle dataset, the work employs a CNN model with an astonishing 92% general accuracy. Stressing amazing accuracy and recall for roses and sunflowers, precision, recall, F1-score, and confusion matrix analysis serves to assess the performance of the model. Although the model excels in many aspects, it might need additional improvement to differentiate common daisies. The results show how successfully CNNs automate floral classification, therefore helping botanical study, commercial floriculture, agricultural management, and floral design. This work provides a good foundation for the next research aiming to increase model accuracy for less represented classes and investigate new techniques to address difficult classification challenges.