Abstract
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.
Highlights
Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae) is one of the most serious pests of stored maize grain worldwide, in tropical and sub-tropical regions [1,2], causing huge economic losses in agricultural and food industry
The radiographic images allowed the detection of the maize weevil in the internal parts of the grains at different developmental stages (Figures 3 and 4)
The peripheral equalization and calcification emphasis algorithms improved the detection of Sitophilus zeamais, regardless of its stage of development (Figures 3 and 4)
Summary
Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae) is one of the most serious pests of stored maize grain worldwide, in tropical and sub-tropical regions [1,2], causing huge economic losses in agricultural and food industry. This weevil attacks several other cereals and agricultural products, processed or not [3]. The early detection of Sitophilus zeamais in grains is very difficult to be achieved in practice because the egg, larva and pupa development occur inside the grain, which is not perceived by the human eye. Different methods have been developed to identify signs of insect infestation during storage such as staining of grains, acoustic techniques, Berlese funnel, uric acid method, grain probes and insect traps [6,7]; these methods are time-consuming and require experienced technicians, and their accuracy
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