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A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contaminated or faulty seal area, only relevant bands are selected using data fusion. These techniques greatly improve the computation time while maintaining a high classification ratio, showing that the fused image contains enough information for checking a food tray sealing state (faulty or normal), avoiding feeding a large image datacube to the DL algorithms. Additionally, the proposed DL algorithms do not require any prior handcraft approach, i.e., no manual tuning of the parameters in the algorithms are required since the training process adjusts the algorithm. The experimental results, validated using an industrial dataset for food trays, along with different deep learning methods, demonstrate the effectiveness of the proposed approach. In the studied dataset, an accuracy of 88.7%, 88.3%, 89.3%, and 90.1% was achieved for Deep Belief Network (DBN), Extreme Learning Machine (ELM), Stacked Auto Encoder (SAE), and Convolutional Neural Network (CNN), respectively.

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This paper proposes an IoT system based on wireless BLE connectivity to monitor the moisture content of wood, using a compact and low-cost moisture device that relies on a resistance measurement method valid for an ultra-wide range of resistance values. This device is digitally controlled with a BLE-incorporated micro-controller characterized by its small size and low power consumption, providing long-life battery. The proposed system consists of two main parts: first, the BLE moisture device including the moisture content measurement and wireless capability (BLE); second, the cloud-based monitoring platform, providing remote visualization and control for all the sensor nodes of the network. The complete infrastructure shows how multiple nodes can read and transmit moisture content of timber in buildings using small and unattended devices, with data saved in a central database and monitored by multiple commercial devices such as PC, smartphone, tablet, etc. The proposed system is innovative, scalable and low cost, and it can be deployed in wooden buildings and the wood industry, providing a practical solution that will help to avoid rot and other damaging effects caused by the moisture content.