Red Onion Seed Quality Classification Using Transfer Learning Approaches

An essential vegetable that is grown all over the world and eaten in a variety of ways is the onion (Allium cepa L.). A common condiment used to improve food flavor is onion. Around the world, red onion seed (A. fistulosum) is cultivated in a variety of temperate and tropical settings. It is grown in China and Japan, among other places, worldwide. 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 datasets collected from different agricultural fields in south Gondar libo kemkem and fogera woreda. We use various augmentation strategies to expand the dataset. Ten percent of the dataset was set aside for testing, ten percent for validation, and eighty percent for training. For VGG19, VGG16, GoogleNet, and ResNet, the model's classification accuracy for the input image is 99%, 100%, 100%, and 86%, respectively.