The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry

In this paper we advocate the application of Artificial Intelligence techniques to quality assessment of food products. Machine Learning algorithms can help us to: (a) extract operative human knowledge from a set of examples; (b) conclude interpretable rules for classifying samples regardless of the non-linearity of the human behaviour or process; and (c) help us to ascertain the degree of influence of each objective attribute of the assessed food on the final decision of an expert. We illustrate these topics with an example of how it is possible to clone the behaviour of bovine carcass classifiers, leading to possible further industrial application

Robotics in meat processing

Scientists are currently investigating micro-robotics in the medical field with a potential to provide better medical technology in the near future. When it comes to the food industry, the use of robots has been traditionally limited to picking and palletization. Today, however, robots are used in material handling and secondary or tertiary packing. Recent developments with faster computers and sophisticated sensors have made it possible to use robotics in the meat processing sectors, where their application has reduced processing costs, occupational injuries, improved efficiency and hygiene associated with meat products. Compared to other industries, the working environment in the meat industry is not very conducive to robotics due to the noisy, damp and cold conditions. Slaughtering animals and cutting meat into pieces and disposing waste is an intensive physically demanding task. This chapter reviews the application of robotics in the meat industry and the advancements that have been made until now

Towards human-robot collaboration in meat processing: Challenges and possibilities

Background Meat is one of the main sources of protein in human nutrition. During recent years meat production volume has been showing significant growth worldwide. The total growth of red meat production is expected to show an 80% increase by 2029, according to the Organisation for Economic Co-operation Development (OECD). Such growth indicates the necessity for existing production line modernisation to satisfy future increased demand for meat products. Scope and approach This article critically reviews automation challenges for robotic applications in the meat industry, among those are heterogeneity of meat pieces and inconsistency of cutting trajectories that must be overcome to achieve the final quality product. It specifically focuses on human-robot collaboration (HRC) that could be applied in the meat industry to address these challenges. The paper elaborates on possible adaptation of HRC in meat industry, based on its achievements in other industries. Key finding and conclusions With increased customisation for both hardware and software robots can offer a flexible, scalable, compact and cost-effective production line alternative to older machinery that require large floor space, are difficult to adapt and include higher maintenance costs. However, in the case of red meat industry there are no off-the-shelf robotic solutions that can cover all the production steps in the secondary meat processing. Introducing collaborative robots into meat processing could help to promote higher standards in food safety and human-working conditions in the industry and make automation more affordable for smaller production plants.Towards human-robot collaboration in meat processing: Challenges and possibilitiespublishedVersio

Integrating structured and unstructured data for imbalanced classification using meat-cut images

The identification of different meat cuts for labeling and quality control on production lines is still largely a manual process. As a result, it is a labor-intensive exercise with the potential for not only error but also bacterial cross-contamination. Artificial intelligence is used in many disciplines to identify objects within images, but these approaches usually require a considerable volume of images for training and validation. The objective of this study was to integrate structured and unstructured data to identify five different meat cuts from images and weights collected by a trained operator within the working environment of a commercial Irish beef plant. The dataset for one of the products exhibited sparsity, resulting in an imbalanced distribution. To rectify this issue, image augmentation techniques were employed to tackle the inherent imbalance within the dataset. Individual cut images and weights from 7,987 meats cuts extracted from semimembranosus muscles (i.e., Topside muscle), post editing, were available. A variety of classical neural networks and a novel Ensemble machine learning approaches were then tasked with identifying each individual meat cut; performance of the approaches was dictated by accuracy (the percentage of correct predictions), precision (the ratio of correctly predicted objects relative to the number of objects identified as positive), and recall (also known as true positive rate or sensitivity). A novel Ensemble approach outperformed a selection of classical neural networks including convolutional neural network and residual network. The accuracy, precision, and recall for the novel Ensemble method were 99.13%, 99.00%, and 98.00%, respectively, while that of the next best method were 98.00%, 98.00%, and 95.00%, respectively. The Ensemble approach, which requires Integrating Structured and Unstructured Data for Imbalanced Classification Using Meat-Cut Images relatively few gold-standard measures, can readily be deployed under normal abattoir conditions; the strategy could also be evaluated in the cuts from other primals or indeed other species such as lamb, chicken, or pork

Image processing techniques to identify predatory birds in aquacultural settings

Bird predation is a major problem in aquaculture. A novel method for dispersing birds is the use of a vehicle that can sense and chase birds. Image recognition software can improve their efficiency in chasing birds. Three recognition techniques were tested to identify birds 1) image morphology 2) artificial neural networks, and 3) template matching have been tested. A study was conducted on three species of birds 1) pelicans, 2) egrets, and 3) cormorants. Images were divided into 3 types 1) Type 1, 2) Type 2, and 3) Type 3 depending upon difficulty to separate from the others in the images. These types were clear, medium clear and unclear respectively. Image morphology resulted in 57.1% to 97.7%, 73.0% to 100%, and 46.1% to 95.5% correct classification rates (CCR) respectively on images of pelicans, cormorants and egrets before size thresholding. The artificial neural network model achieved 100% CCR while testing type 1 images and its classification success ranged from 63.5% to 70.0%, and 57.1% to 67.7% while testing type 2 and type 3 images respectively. The template matching algorithm succeeded in classifying 90%, 80%, and 60% of Type 1, Type 2 and Type 3 images of pelicans and egrets. This technique recognized 80%, 91.7%, and 80% of Type 1, Type 2, and Type 3 images of cormorants. We developed a real time recognition algorithm that could capture images from a camera, process them, and send output to the autonomous boat in regular intervals of time. Future research will focus on testing the recognition algorithms in natural or aquacultural settings on autonomous boats

Towards human-robot collaboration in meat processing: Challenges and possibilities

Background: Meat is one of the main sources of protein in human nutrition. During recent years meat production volume has been showing significant growth worldwide. The total growth of red meat production is expected to show an 80% increase by 2029, according to the Organisation for Economic Co-operation Development (OECD). Such growth indicates the necessity for existing production line modernisation to satisfy future increased demand for meat products. Scope and approach: This article critically reviews automation challenges for robotic applications in the meat industry, among those are heterogeneity of meat pieces and inconsistency of cutting trajectories that must be overcome to achieve the final quality product. It specifically focuses on human-robot collaboration (HRC) that could be applied in the meat industry to address these challenges. The paper elaborates on possible adaptation of HRC in meat industry, based on its achievements in other industries. Key finding and conclusions: With increased customisation for both hardware and software robots can offer a flexible, scalable, compact and cost-effective production line alternative to older machinery that require large floor space, are difficult to adapt and include higher maintenance costs. However, in the case of red meat industry there are no off-the-shelf robotic solutions that can cover all the production steps in the secondary meat processing. Introducing collaborative robots into meat processing could help to promote higher standards in food safety and human-working conditions in the industry and make automation more affordable for smaller production plants