Abstract

Abstract: Vegetables that are popular and versatile over the world are peppers. Precise categorisation of pepper cultivars is vital for multiple uses, such as assessing market trends, regulating quality, and conducting genetic research. Classifying peppers using traditional methods can be subjective and time-consuming. This research proposes an automated pepper variety classification method based on deep learning. A deep convolutional neural network (CNN) model was trained on a dataset of 2,368 photos of peppers. With the purpose of accurately classifying the pepper photos, the CNN was built to extract essential features from them. The trained model demonstrated its efficacy in differentiating between pepper varieties with an astounding accuracy of 100% on a held-out test set. Deep learning has the capacity to classify peppers accurately and efficiently, as this study shows. The suggested strategy can help the food processing, quality control, and agriculture sectors advance.