Seed genuineness and varietal purity are key indicators of seed quality. Detecting the genuineness of a single seed can simultaneously determine seed purity. The traditional methods for detecting seed genuineness or identifying a variety are time-consuming, costly, and destructive. This study intends to establish a low-cost, efficient, and non-destructive method to detect the genuineness of single maize seeds, based on RGB images combined with deep learning. Eight hundred maize seeds of JINGKE 968 from different lots in different years and 800 seeds of other varieties were selected. Scanned images of both the germ and non-germ surfaces of the seeds were collected. The images were divided into a training set and a validation set according to the ratio of 7:3. A total of 17,600 images were obtained after data augmentation. The VGG16 network was used for transfer learning after fine-tuning, to identify and classify the seed images, and then to establish the model to detect the genuineness of the maize variety 'JINGKE 968′. The results show that the optimal detection accuracy was over 99%, and the model loss was maintained at about 0.05. Another 100 suspected samples were tested, and the recognition accuracy was as high as 98%. In summary, this study provided a non-destructive, highly efficient, fairly reliable, simple and cost saving method to identify true and false individuals of JINGKE 968. These results can serve as a reference to identify the genuineness of seeds for other crops.

种子的真实性和品种纯度是种子质量的关键指标。检测单个种子的真实性可以同时确定种子的纯度。用于检测种子真伪或识别品种的传统方法耗时，昂贵且具有破坏性。这项研究旨在基于RGB图像结合深度学习，建立一种低成本，高效且无损的方法来检测单粒玉米种子的真伪。选择了不同年份不同批次的京科968玉米种子800个，其他品种的800个种子。收集种子的胚芽和非胚芽表面的扫描图像。根据7：3的比例将图像分为训练集和验证集。数据扩充后，总共获得了17,600张图像。VGG16网络经过微调后用于转移学习，对种子图像进行识别和分类，然后建立检测玉米品种“精科968”真伪的模型。结果表明，最优检测精度超过99％，模型损失保持在0.05左右。测试了另外100个可疑样本，识别准确率高达98％。总而言之，本研究提供了一种无损，高效，可靠，简单且节省成本的方法来鉴定真科968的真假。这些结果可作为鉴定其他农作物种子真伪的参考。结果表明，最优检测精度超过99％，模型损失保持在0.05左右。测试了另外100个可疑样本，识别准确率高达98％。总而言之，本研究提供了一种无损，高效，可靠，简单且节省成本的方法来鉴定真科968的真假。这些结果可作为鉴定其他农作物种子真伪的参考。结果表明，最优检测精度超过99％，模型损失保持在0.05左右。测试了另外100个可疑样本，识别准确率高达98％。总而言之，本研究提供了一种无损，高效，可靠，简单且节省成本的方法来鉴定真科968的真假。这些结果可作为鉴定其他农作物种子真伪的参考。