Abstract

The quality of onions (Allium cepa), a vegetable that is consumed worldwide, is essential for food safety and agricultural economics. This study suggests a deep learning-based approach that uses convolutional neural networks (CNNs) to categorize images of onions as either healthy or unhealthy. The technique uses a proposed architecture and a Customized Dataset (CDS) and publically accessible sources like Fruit360, Onion-det, and Vegetable360. The model, which outperformed the others, reached a peak accuracy of 98.33% on the CDS dataset. The study also highlights the importance of color information in onion disease categorization, with models trained on RGB images performing better than monochrome counterparts. The model’s classification skills are further confirmed using confusion matrices. The CNN architecture has a lot of potential for automated onion quality evaluation, outperforming conventional pre-trained models regarding resilience and dependability, delivering high classification accuracy and great generalization across various datasets and images circumstances.

Authors

J. Anusha Jajur , U. Kumar Siddamallappa
Davangere University, India

Keywords

CNN, MobileNetV2, VGG16, ResNet50