Oat grains classification using deep learning

Abstract

Background: Based on their nutritional benefits, oat is classified as a cereal of great importance for both human and animal feeding. Throughout the production process, species and variety identification are vital for agricultural systems. The present work establishes SeedFlow, a method for image acquisition, processing, and classification of oat grains using deep learning techniques. We apply these techniques to the identification of the grains from the different oat species Avena sativa and Avena strigosa and to classify grains as varieties of Avena sativa, such as UPFA Ouro, UPFA Fuerza, and UPFA Gaudéria. Results: To achieve this proposition, we executed our solution considering six different deep learning architectures to evaluate which model presents the best performance. This approach attained an accuracy of 99.7% for oat species identification and 89.7% for oat varieties classification using DenseNet architecture. Conclusions: As a result, this tool can provide high value for practical quality control applications, and it is feasible to use in pre-screening tests, laboratory analysis pipelines, or computer support tools geared toward breeding programs or intellectual property assessment.