Hyperspectral image classification using CNN: Application to industrial food packaging

Medus, Leandro D.; Saban, Mohamed; Francés-Víllora, Jose V.; Bataller-Mompeán, Manuel; Muñoz, Andrés Pedreño

doi:10.1016/j.foodcont.2021.107962

Cited by 83 publications

(38 citation statements)

References 24 publications

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“…In 2021 alone, deep learning neural networks have been used to classify beef freshness from visible-NIR reflectance spectra [60], to analyze NIR HSI to detect the presence of contamination during food packing [61], and to conduct a series of different food quality analyses from NIR spectra [62].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Advanced Optical Technologies in Food Quality and Waste Management

Chauvin¹,

Duran²,

Ng³

et al. 2021

Innovation in the Food Sector Through the Valorization of Food and Agro-Food by-Products

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Food waste is a global problem caused in large part by premature food spoilage. Seafood is especially prone to food waste because it spoils easily. Of the annual 4.7 billion pounds of seafood destined for U.S. markets between 2009 and 2013, 40 to 47 percent ended up as waste. This problem is due in large part to a lack of available technologies to enable rapid, accurate, and reliable valorization of food products from boat or farm to table. Fortunately, recent advancements in spectral sensing technologies and spectroscopic analyses show promise for addressing this problem. Not only could these advancements help to solve hunger issues in impoverished regions of the globe, but they could also benefit the average consumer by enabling intelligent pricing of food products based on projected shelf life. Additional technologies that enforce trust and compliance (e.g., blockchain) could further serve to prevent food fraud by maintaining records of spoilage conditions and other quality validation at all points along the food supply chain and provide improved transparency as regards contract performance and attribution of liability. In this chapter we discuss technologies that have enabled the development of hand-held spectroscopic devices for detecting food spoilage. We also discuss some of the analytical methods used to classify and quantify spoilage based on spectral measurements.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Advanced Optical Technologies in Food Quality and Waste Management

Chauvin¹,

Duran²,

Ng³

et al. 2021

Innovation in the Food Sector Through the Valorization of Food and Agro-Food by-Products

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“…• Biological image classification (Qin et. al, 2020) • Brain tumor detection (Özyurt et al, 2019; Özyurt et al, 2020) and diagnosis system (Sert et al, 2019) • Chinese text recognition (Wang and Du, 2021) • Facial expression recognizer (Teja et al, 2020) • Finger vein recognition (Zhao et al, 2020) • Industrial food packaging (Medus et al, 2021) • Leaf disease classification (Deeba and Amutha, 2020) • Speech emotion recognition (Kwon, 2020) • Underwater target detection (Zeng et al, 2021) In the present study, Faster R-CNN Object Detection Approach with GoogLeNet Classifier (Faster R-CNN-GC) approach, which combined Faster R-CNN and GoogLeNet, was proposed in order to detect pepper and potato leaves and diseases. Some images were combined using an image stitching (Brown and Lowe, 2007) approach because they consisted of two pieces, which helped perform image processing on some leaf images from a wider angle.…”

Section: Introductionmentioning

confidence: 99%

A deep learning based approach for the detection of diseases in pepper and potato leaves

Sert¹

2021

Anadolu Journal of Agricultural Sciences

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The present study proposes a Faster R-CNN Object Detection Approach with GoogLeNet Classifier (Faster R-CNN-GC) using image stitching, Faster R-CNN and GoogLeNet to detect pepper and potato leaves as well as leaf diseases in them. It is widely known that for a successful object detection performance, Faster R-CNN requires performing image labelling on a very high number of data, which will later train Faster R-CNN. However, this process is often very time-consuming. The present study mainly aims to shorten this process by designing an object detection approach which benefits from Faster R-CNN and GoogLeNet architecture. Firstly, Faster R-CNN and GoogLeNet were trained. Later, for the testing process, some of two-piece images were combined using an image stitching approach. Finally, using Faster R-CNN and GoogLeNet, pepper and potato leaves are detected and diseases are written on them. In addition, the proposed system was compared with Faster R-CNN Object Detection Approach with AlexNet Classifier (Faster R-CNN-AC), Faster R-CNN Object Detection Approach with SequezeNet Classifier (Faster R-CNN-SC) and Faster R-CNN. The findings of the experimental studies demonstrated that Faster R-CNN-GC displayed a higher object detection performance compared to other approaches.Biber ve patates yapraklarındaki hastalıkların saptanması için derin öğrenme temelli bir yaklaşım

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“…The application of computer vision and artificial intelligence techniques allows this process to be done automatically. Medus et al (2021) 11 , for instance, proposed a system which, by using a convolutional neural network (CNN) as a classifier in heat-sealed food trays, is able to automatically detect anomalies during the packaging process in order to discard the faulty tray and avoid human consumption. Thota et al (2020) 12 presented a multi-source deep-learning-based domain adaptation system to identify and verify the presence and legibility of use-by date information from food packaging photos taken as part of the validation process as the products pass along the food production line.…”

Section: Introductionmentioning

confidence: 99%

“…Material properties such as transparency or serigraphy make the detection of sealing faults via computer vision systems difficult. Several systems have been proposed to classify, control, or identify sealing defects by using polarized light stress analysis and laser scatter imaging 40 , active infrared thermography 41 or hyperspectral imaging 11 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning for the quality control of thermoforming food packages

Banús

Boada

Xiberta

et al. 2021

Sci Rep

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Quality control is a key process designed to ensure that only products satisfying the defined quality requirements reach the end consumer or the next step in a production line. In the food industry, in the packaging step, there are many products that are still evaluated by human operators. To automate the process and improve efficiency and effectiveness, computer vision and artificial intelligence techniques can be applied. This automation is challenging since specific strategies designed according to the application scenario are required. Focusing on the quality control of the sealing and closure of matrix-shaped thermoforming food packages, the aim of the article is to propose a deep-learning-based solution designed to automatically perform the quality control while satisfying production cadence and ensuring 100% inline inspection of the products. Particularly, the designed computer vision system and the image-based criteria defined to determine when a product has to be accepted or rejected are presented. In addition, the vision control software is described with special emphasis on the different convolutional neural network (CNN) architectures that have been considered (ResNet18, ResNet50, Vgg19 and DenseNet161, non-pre-trained and pre-trained on ImageNet) and on the specifically designed dataset. To test the solution, different experiments are carried out in the laboratory and also in a real scenario, concluding that the proposed CNN-based approach improves the efficiency and security of the quality control process. Optimal results are obtained with the pre-trained DenseNet161, achieving false positive rates that range from 0.03 to 0.30% and false negative rates that range from 0 to 0.07%, with a rejection rate between 0.64 and 5.09% of production, and being able to detect at least 99.93% of the sealing defects that occur in any production. The modular design of our solution as well as the provided description allow it to adapt to similar scenarios and to new deep-learning models to prevent the arrival of faulty products to end consumers by removing them from the automated production line.

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Hyperspectral image classification using CNN: Application to industrial food packaging

Cited by 83 publications

References 24 publications

Advanced Optical Technologies in Food Quality and Waste Management

Advanced Optical Technologies in Food Quality and Waste Management

A deep learning based approach for the detection of diseases in pepper and potato leaves

Deep learning for the quality control of thermoforming food packages

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