Quantity and quality of grain are both closely related to national development and social stability. Grain is lost during storage due to mildew and insects. Detection of damaged grain kernels not only can reduce the loss of grain, but also protect human beings from diseases caused by damaged grain. Therefore, research on the automatic detection of damaged grain is of continued urgency. In this paper, we propose a framework combining spectrogram generative adversarial network and progressive neural architecture search (SPGAN-PNAS) to detect and classify mildew-damaged wheat kernels (MDK), insect-damaged wheat kernels (IDK) and undamaged wheat kernels (UDK). First, the spectrogram generative adversarial network (SPGAN) is designed to enlarge the data set. Second, we apply progressive neural architecture search (PNAS) to generate network structure to classify three types of wheat kernels. An F1 of 96.2% is obtained using the proposed method with 5-fold cross-validation. The results are superior to the classical neural networks for detection and classification of damaged wheat kernels. Experimental results show that the structure of SPGAN-PNAS is feasible and effective.

粮食的数量和质量都与国家发展和社会稳定密切相关。由于霉菌和昆虫，谷物在储存过程中会丢失。检测破损粮粒不仅可以减少粮食损失，还可以保护人类免受破损粮谷疾病的侵害。因此，对破损谷物自动检测的研究具有持续的紧迫性。在本文中，我们提出了一种结合频谱图生成对抗网络和渐进式神经架构搜索 (SPGAN-PNAS) 的框架来检测和分类受霉变损坏的小麦粒 (MDK)、受虫害的小麦粒 (IDK) 和未损坏的小麦粒 (UDK)。 ）。首先，频谱图生成对抗网络（SPGAN）旨在扩大数据集。第二，我们应用渐进式神经架构搜索（PNAS）来生成网络结构来对三种类型的小麦籽粒进行分类。使用具有 5 折交叉验证的建议方法获得 96.2% 的 F1。结果优于经典的神经网络，用于检测和分类受损小麦籽粒。实验结果表明SPGAN-PNAS的结构是可行和有效的。