Artificial Intelligence Development and Accuracy Evaluation for Green Soybean Appearance Quality Sorting Using Deep Learning (Part 1)

—Effectiveness of Green Soybean Appearance Quality Sorting Using Object Detection—

Abstract

Most farmers who cultivate green soybeans use manual sorting, with work efficiency of 12 kg/h. To improve this low work efficiency, a sorting machine must be developed for rapid accurate detection and classification of their appearance quality. An object detection artificial intelligence (AI) was developed for sorting green soybeans. Methods to achieve high performance were discussed. An AI developed with a dataset including one variety had higher precision, recall, and a higher F-value than an AI created with a dataset including data from multiple varieties. The highest Newton efficiency was 0.57, equivalent to manual sorting, was obtained by inclusion of good and defective product images in the dataset. Results confirmed the effectiveness of AI-based object detection for sorting green soybeans.