Red Onion Seed Quality Classification Using Transfer Learning Approaches

Abstract

Onion (Allium cepa L.) is a very important vegetable grown all over the world and consumed in various forms. Onion is widely used as a condiment to enhance the flavor of food. Red onion seed (A. fistulosum) is grown throughout the world in the wide range of climates temperate to tropical conditions. Globally, it is cultivated in moreover China and Japan. A. fistulosum is grown across Ethiopia in various regions. In 2012, 3,281,574 tons of output were obtained from 30,478 hectares of coverage. Allium fistulosum covers the Amhara area over 8000 hectors, which is 26% of our country. For export, red onion seed is separated based on quality. Red onion seed quality separation or categorization is essential to the trade process. It aids in making people marketable. In Ethiopia, this procedure is carried out manually, which has a number of drawbacks like being less effective, inconsistent, and prone to subjectivity. To address this problem we use pre-trained transfer learning model VGG, GoogleNet, and ResNet50 for quality classification of red onion seed. The main procedures include image preprocessing, resizing, data augmentation, and prediction. The model trained on 470 dataset collected from different agricultural fields in south Gondar libo kemkem and fogera woreda. To increase the dataset we apply different augmentation techniques. We split the dataset into 80% for training, 10% for validation and 10% for testing. The model classifies the input image with 99%, 100%, 100% an