A methodology for the selection of industrial robots in food handling

Unveiling the relationship between food unit operations and food industry 4.0: A short review

The fourth industrial revolution (Industry 4.0) is driving significant changes across multiple sectors, including the food industry. This review examines how Industry 4.0 technologies, such as smart sensors, artificial intelligence, robotics, and blockchain, among others, are transforming unit operations within the food sector. These operations, which include preparation, processing/transformation, preservation/stabilization, and packaging and transportation, are crucial for converting raw materials into high-quality food products. By incorporating advanced digital, physical, and biological innovations, Industry 4.0 technologies are enhancing precision, productivity, and environmental responsibility in food production. The review highlights innovative applications and key findings that showcase how these technologies can streamline processes, minimize waste, and improve food product quality. The adoption of Industry 4.0 innovations is increasingly reshaping the way food is prepared, transformed, preserved, packaged, and transported to the final consumer. The work provides a valuable roadmap for various sectors within agriculture and food industries, promoting the adoption of Industry 4.0 solutions to enhance efficiency, quality, and sustainability throughout the entire food supply chain.

A Simulation-Based Approach for Evaluating the Effectiveness of Robotic Automation Systems in HMR Product Loading

The food industry has tried to enhance production processes in response to the increasing demand for safe, high-quality Home Meal Replacement (HMR) products. While robotic automation systems are recognized for their potential to improve efficiency, their high costs and risks make them less accessible to small and medium-sized enterprises (SMEs). This study presents a simulation-based approach to evaluating the feasibility and impact of robotic automation on HMR production, focusing on two distinct production cases. By modeling large-scale and order-based production cases using simulation software, the study identified key bottlenecks, worker utilization, and throughput improvements. It demonstrated that robotic automation increased throughput by 31.2% in large-scale production (Case A) and 12.0% in order-based production (Case B). The actual implementation showed results that closely matched the simulation, validating the approach. Moreover, the study confirmed that a single worker could operate the robotic system effectively, highlighting the practicality of robotics for SMEs. This research provides critical insights into integrating robotics to enhance productivity, reduce labor dependency, and facilitate digital transformation in food manufacturing.

Avenues for non-conventional robotics technology applications in the food industry

Robots in manufacturing alleviate hazardous environmental conditions, reduce the physical/mental stress of the workers, maintain high precision for repetitive movements, reduce errors, speed up production, and minimize production costs. Although robots have pervaded many industrial sectors and domestic environments, the experiments in the food sectors are limited to pick-and-place operations and meat processing while we are assisting new attention in gastronomy. Given the great performances of the robots, there would be many other intriguing applications to explore which could usher the transition to precision food manufacturing. This review wants open thoughts and opinions on the use of robots in different food operations. First, we reviewed the recent advances in common applications - e.g. novel sensors, end-effectors, and robotic cutting. Then, we analyzed the use of robots in other operations such as cleaning, mixing/kneading, dough manipulation, precision dosing/cooking, and additive manufacturing. Finally, the most recent improvements of robotics in gastronomy with their use in restaurants/bars and domestic environments, are examined. The comprehensive analyses and the critical discussion highlighted the needs of further scientific understanding and exploitation activities aimed to fill the gap between the laboratory-scale results and the validation in the relevant environment.

Advancement in artificial intelligence for on-farm fruit sorting and transportation

On-farm sorting and transportation of postharvest fruit include sorting out defective products, grading them into categories based on quality, distributing them into bins, and carrying bins to field collecting stations. Advances in artificial intelligence (AI) can speed up on-farm sorting and transportation with high accuracy and robustness and significantly reduce postharvest losses. The primary objective of this literature review is to provide an overview to present a critical analysis and identify the challenges and opportunities of AI applications for on-farm sorting and transportation, with a focus on fruit. The challenges of on-farm sorting and transportation were discussed to specify the role of AI. Sensors and techniques for data acquisition were investigated to illustrate the tasks that AI models have addressed for on-farm sorting and transportation. AI models proposed in previous studies were compared to investigate the adequate approaches for on-farm sorting and transportation. Finally, the advantages and limitations of utilizing AI have been discussed, and in-depth analysis has been provided to identify future research directions. We anticipate that this survey will pave the way for further studies on the implementation of automated systems for on-farm fruit sorting and transportation.

Scalable Biofabrication: A Perspective on the Current State and Future Potentials of Process Automation in 3D-Bioprinting Applications

Biofabrication, specifically 3D-Bioprinting, has the potential to disruptively impact a wide range of future technological developments to improve human well-being. Organs-on-Chips could enable animal-free and individualized drug development, printed organs may help to overcome non-treatable diseases as well as deficiencies in donor organs and cultured meat may solve a worldwide environmental threat in factory farming. A high degree of manual labor in the laboratory in combination with little trained personnel leads to high costs and is along with strict regulations currently often a hindrance to the commercialization of technologies that have already been well researched. This paper therefore illustrates current developments in process automation in 3D-Bioprinting and provides a perspective on how the use of proven and new automation solutions can help to overcome regulatory and technological hurdles to achieve an economically scalable production.

Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping

Instance segmentation of overlapping plants to detect their grasps for possible robotic grasping presents a challenging task due to the need to address the problem of occlusion. We addressed the problem of occlusion using a powerful convolutional neural network for segmenting objects with complex forms and occlusions. The network was trained with a novel dataset named the “occluded plants” dataset, containing real and synthetic images of plant cuttings on flat surfaces with differing degrees of occlusion. The synthetic images were created using the novel framework for synthesizing 2D images by using all plant cutting instances of available real images. In addition to the method for occlusion handling for overlapped plants, we present a novel method for determining the grasps of segmented plant cuttings that is based on conventional image processing. The result of the employed instance segmentation network on our plant dataset shows that it can accurately segment the overlapped plants, and it has a robust performance for different levels of occlusions. The presented plants’ grasp detection method achieved 94% on the rectangle metric which had an angular deviation of 30 degrees and an IoU of 0.50. The achieved results show the viability of our approach on plant species with an irregular shape and provide confidence that the presented method can provide a basis for various applications in the food and agricultural industries.

The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies

Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and other technological advances (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work. The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move toward Industry 5.0.

Challenges and Opportunities in Robotic Food Handling: A Review

Despite developments in robotics and automation technologies, several challenges need to be addressed to fulfill the high demand for automating various manufacturing processes in the food industry. In our opinion, these challenges can be classified as: the development of robotic end-effectors to cope with large variations of food products with high practicality and low cost, recognition of food products and materials in 3D scenario, better understanding of fundamental information of food products including food categorization and physical properties from the viewpoint of robotic handling. In this review, we first introduce the challenges in robotic food handling and then highlight the advances in robotic end-effectors, food recognition, and fundamental information of food products related to robotic food handling. Finally, future research directions and opportunities are discussed based on an analysis of the challenges and state-of-the-art developments.

Advanced Applications of Industrial Robotics: New Trends and Possibilities

This review is dedicated to the advanced applications of robotic technologies in the industrial field. Robotic solutions in areas with non-intensive applications are presented, and their implementations are analysed. We also provide an overview of survey publications and technical reports, classified by application criteria, and the development of the structure of existing solutions, and identify recent research gaps. The analysis results reveal the background to the existing obstacles and problems. These issues relate to the areas of psychology, human nature, special artificial intelligence (AI) implementation, and the robot-oriented object design paradigm. Analysis of robot applications shows that the existing emerging applications in robotics face technical and psychological obstacles. The results of this review revealed four directions of required advancement in robotics: development of intelligent companions; improved implementation of AI-based solutions; robot-oriented design of objects; and psychological solutions for robot–human collaboration.

Consumer attitudes to vertical farming (indoor plant factory with artificial lighting) in China, Singapore, UK, and USA: A multi-method study

Major changes are needed both with regard to what we eat and how food is produced. The latter is the focus of the present research, specifically the rise of controlled environment agriculture. In this context, empirical research is presented on consumer attitudes to vertical farming (VF) (i.e., indoor plant factory with artificial lighting), conducted in four countries (USA, UK, Singapore, and China) using online surveys (637-683 participants per country with matched gender and age group distributions). A multi-method research approach was used, including a novel methodology of text highlighting, which requires that participants read a descriptive text about VF with mentions of pros and cons and use highlighter functions to select aspects of the text that they 'like' and 'dislike'. Based on the information provided in the text, attitudes towards VF were largely positive in the four countries. The characteristics of VF that aligned with the United Nations Sustainable Development Goals were identified as key drivers of positive attitudes (i.e., higher yield, reduction of carbon emissions, and securing access to food). On the other hand, high energy use and premium prices contributed to negative attitudes about VF. Although the majority of participants responded to the text with an overall positive attitude towards VF, there were smaller groups of participants in every country who expressed a negative or neutral/ambivalent attitude. These between-segment differences were larger than cross-cultural differences, although the latter did exist, particularly for selected aspects of VF. For example, Chinese participants tended to be the least negative about the use of robots to help planting and harvesting. Future research is needed to understand consumer responses to aspects VF not covered in the text (e.g., powering VF with renewable energy, product range), and consumer insights about VF should be sought in other countries.