Barboza da Silva Clíssia, Silva Alysson Alexander Naves, Barroso Geovanny, Yamamoto Pedro Takao, Arthur Valter, Toledo Claudio Fabiano Motta, Mastrangelo Thiago de Araújo
Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba 13416-000, SP, Brazil.
Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13560-970, SP, Brazil.
Foods. 2021 Apr 16;10(4):879. doi: 10.3390/foods10040879.
The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection.
深度学习等人工智能（AI）在谷物质量控制中的应用有潜力协助分析人员进行决策并改进流程。基于X射线成像的先进技术提供了明显更简便的方法来控制储存产品的虫害，无论谷物表面的质量特征是否可见。在此，我们对X射线图像应用了基于周边均衡和钙化增强的对比度增强算法，以改进玉米籽粒中[此处原文缺失相关虫害名称]的检测。此外，我们提出了一种基于卷积神经网络（CNN）的方法，使用三种不同架构来识别未受虫害和受虫害的类别；（i）Inception-ResNet-v2，（ii）Xception和（iii）MobileNetV2。总体而言，基于MobileNetV2和Xception架构开发的预测模型在识别未受虫害的谷物和受玉米象虫害的谷物方面取得了更高的准确率（≥0.88），验证集和测试集的正确分类率在0.78至1.00之间。因此，所提出的使用增强射线照片的方法有潜力为玉米籽粒的安全食用提供精确的[此处原文缺失相关虫害名称]控制。所提出的方法可以在无需人工特征的情况下自动识别被隐藏储存害虫污染的食品，这使得它在谷物检验中更可靠。
