文章基本信息

标题：Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain
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作者：Clíssia Barboza da Silva ; Alysson Alexander Naves Silva ; Geovanny Barroso 等
期刊名称：Foods
电子版ISSN：2304-8158
出版年度：2021
卷号：10
期号：4
页码：879
DOI：10.3390/foods10040879
出版社：MDPI Publishing
摘要：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 Sitophilus zeamais 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 Sitophilus zeamais 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.
关键词：deep learning architectures; MobileNetV2; Xception; Inception-ResNet-v2; peripheral equalization; calcification emphasis; transfer learning; ImageNet; X-ray imaging; maize weevil deep learning architectures ; MobileNetV2 ; Xception ; Inception-ResNet-v2 ; peripheral equalization ; calcification emphasis ; transfer learning ; ImageNet ; X-ray imaging ; maize weevil