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.

准确分选优质大豆种子是育种行业提高大豆产量的关键任务。目前，基于机器视觉的分拣系统只关注一个侧面的识别。本文设计并开发了一种基于深度学习的分选系统，可以识别大豆种子的全表面。交替的旋转机制用于暴露种子的完整表面特征信息，并应用深度学习模型进行准确的种子分类。我们将大豆种子分为六类。在三个亮度环境和六个表面中收集和屏蔽图像，并对大豆种子的缺陷尺度进行量化。我们比较和测试了七个 CNN 模型，并以最佳的整体性能改进模型。采用可视化技术评估不同模型对大豆种子缺陷的识别性能，并根据结果对模型进行优化，实现不同尺度下种子缺陷的准确分类。测试过程表明，所有模型在中等亮度条件下的准确度最高。MobileNetV2改进后的模型在masked数据集的分类准确率达到97.84%，搭配NVIDIA Jetson Nano板的推理速度达到35 FPS，实现了大豆全表面的实时识别。本文提出的分选系统可以实现高精度、低成本的应用，总分选准确率为98.87%，分选速度为每分钟222粒种子。该方法可作为大豆种子精确分选的有效工具。而且，