Real-time recognition system of soybean seed full-surface defects based on deep learning

Abstract

Accurately sorting high-quality soybean seeds is a key task in increasing soybean yield in the breeding industry. At present, sorting systems based on machine vision focus on the recognition of only one side surface. This paper designs and develops a sorting system based on deep learning that can recognize the full surface of soybean seeds. An alternate circumrotating mechanism is used to expose the full surface feature information of the seeds, and a deep learning model is applied for accurate seed classification. We divide soybean seeds into six categories. Images are collected and masked in three brightness environments and six surfaces, and a defect scale of soybean seeds is quantified. We compare and test seven CNN models and improve the model with the best overall performance. Visualization technology is used to assess the recognition performance of different models for soybean seed defects, and the model is optimized based on the results to achieve accurate classification of seed defects at different scales. The testing process indicates that all the models have the highest accuracy under medium brightness conditions. The classification accuracy of the MobileNetV2-improved model reaches 97.84% in the masked dataset and has an inference speed of 35 FPS with NVIDIA's Jetson Nano board, realizing real-time recognition of the soybean full surface. The sorting system proposed in this paper can achieve high-precision and low-cost application, with a total sorting accuracy of 98.87% and a sorting speed of 222 seeds per minute. This method can be used as an effective tool for precise sorting of soybean seeds. Moreover, it provides an approach for full-surface detection of defective ellipsoid seeds of different scales.