BeanClassify: Convolutional Neural Networks for Coffee Bean Categorization

In the coffee industry, bean classification is essential for quality assurance and management. Conventional approaches are frequently laborious and subjective, and they mostly rely on human expertise. Article provide a unique method for automatically classifying coffee beans crop from photos using Convolutional Neural Networks (CNNs). The article gathered a varied dataset that included high-quality photos of many coffee bean varieties and grades. To improve feature extraction and standardize the images, preprocessing approaches were used. An architecture was created, with several convolutional layers, max-pooling, and fully linked layers coming after. The dataset was separated into training, validation, and test sets to train and assess the CNN model. Performance measures like recall, accuracy, precision, and F1-score were computed to gauge how well the model classified coffee beans. The test results show that the suggested method for classifying coffee beans is effective. The model demonstrated its potential for useful implementation in the coffee business by achieving high accuracy and outperforming conventional methods. To improve classification efficiency and accuracy, future work may entail investigating new image processing methods and further refining the CNN architecture.