Birth of dairy 4.0: Opportunities and challenges in adoption of fourth industrial revolution technologies in the production of milk and its derivatives

AI and Data Analytics in the Dairy Farms: A Scoping Review

The strong growth of the world population will cause a major increase in demand for bovine milk, making it necessary to use various technologies to increase milk production efficiently. Some technologies that can contribute to solving part of this problem are those related to data analytics tools, big data, and sensor development. It is timely to review modern technologies and data analytics methods for milk predictions in view of supporting decision-making in dairy farms. To this end, a scoping review was carried out, which resulted in 151 articles of interest. Among the most important results, we found that (i) the identified studies are relatively recent with an average publication time of 5.95 years; (ii) the scope of the selected studies is mostly concentrated on milk and prediction (29%), early detection of lameness (26%), and timely detection of mastitis (13%); (iii) the type of analysis is mostly predictive (87%), and prescriptive is barely present (3%); (iv) the types of input data used in the studies are preferably historical (70%), and real-time data (25%) are used less frequently; (v) we found that the method of artificial neural networks (47%) and the convolutional neural networks (24%) are the most used for the studies regarding bovine milk output predictions. In the selected studies, the artificial neural network methods have considerable accuracy, recall, precision, and F1 Scores on average but with high ranges and standard deviations. (vi) Simulation tools are scarcely used, being present in 4% of cases. In the treatment of variability, the models reviewed are mostly deterministic (77%), and the stochastic models (5%) are considered in a small number of cases. Based on our analysis, we conclude that future research on decision-making tools will benefit from the advantages of artificial neural networks in data analytics combined with optimization-simulation methods.

From tradition to precision: leveraging digital tools to improve cattle health and welfare

Traditional cattle production practices relied heavily on manual observation and empirical decision-making, often leading to inconsistent outcomes. In contrast, modern approaches leverage technology to achieve greater precision and efficiency. Advancement in technology has shifted to a new dimension of predictive and monitoring in cattle health management. This review aims at highlighting the available and current digital technologies in cattle health, evaluate their utility in practice, and identify possible future advancements in the field that can potentially bring even more changes to this industry. The paper highlights some of the barriers and disadvantages of using these technologies, such as data security issues, high capital investments, and skills gap. The integration of these advanced technologies is set to play a fundamental role in enabling the livestock industry to meet the rising global demand for high-quality, sustainably produced products. These technologies are essential for ensuring compliance with ethical standards and best practices in cattle care and well-being. In light of these advancements, the application of digital innovations will support the achievement of socially responsible cattle production, while simultaneously maintaining optimal levels of animal health and welfare.

Enhancing safety and quality in the global cheese industry: A review of innovative preservation techniques

The global cheese industry faces challenges in adopting new preservation methods due to microbiological decay and health risks associated with chemical preservatives. Ensuring the safety and quality control of hard and semi-hard cheeses is crucial given their prolonged maturation and storage. Researchers are urged to create cheese products emphasizing safety, minimal processing, eco-labels, and clean labels to address consumer health and environmental worries. This review aims to explore effective strategies for ensuring the safety and quality of ripened cheeses, covering traditional techniques like aging, maturation, and salting, along with innovative methods such as modified and vacuum packaging, high-pressure processing, and active and intelligent packaging. Additionally, sustainable cheese preservation approaches, their impact on shelf life extension, and the physiochemical and quality attributes post-preservation are all analyzed. Overall, the cheese industry stands to benefit from this evaluation through enhanced market value, increased consumer satisfaction, and better environmental sustainability.The integration of novel preservation techniques in the cheese industry not only addresses current challenges but also paves the way for a more sustainable and consumer-oriented approach. By continually refining and implementing safety measures, quality control processes, and environmentally friendly practices, cheese producers can meet evolving consumer demands while ensuring the longevity and integrity of their products. Through a concerted effort to embrace innovation and adapt to changing market dynamics, the global cheese industry is poised to thrive in a competitive landscape where safety, quality, and sustainability are paramount.

Utilization of AI - reshaping the future of food safety, agriculture and food security - a critical review

Artificial intelligence is an emerging technology which harbors a suite of mechanisms that have the potential to be leveraged for reaping value across multiple domains. Lately, there is an increased interest in embracing applications associated with Artificial Intelligence to positively contribute to food safety. These applications such as machine learning, computer vision, predictive analytics algorithms, sensor networks, robotic inspection systems, and supply chain optimization tools have been established to contribute to several domains of food safety such as early warning of outbreaks, risk prediction, detection and identification of food associated pathogens. Simultaneously, the ambition toward establishing a sustainable food system has motivated the adoption of cutting-edge technologies such as Artificial Intelligence to strengthen food security. Given the myriad challenges confronting stakeholders in their endeavors to safeguard food security, Artificial Intelligence emerges as a promising tool capable of crafting holistic management strategies for food security. This entails maximizing crop yields, mitigating losses, and trimming operational expenses. AI models present notable benefits in efficiency, precision, uniformity, automation, pattern identification, accessibility, and scalability for food security endeavors. The escalation in the global trend for adopting alternative protein sources such as edible insects and microalgae as a sustainable food source reflects a growing recognition of the need for sustainable and resilient food systems to address the challenges of population growth, environmental degradation, and food insecurity. Artificial Intelligence offers a range of capabilities to enhance food safety in the production and consumption of alternative proteins like microalgae and edible insects, contributing to a sustainable and secure food system.

Thermographic assessment of mastitis progression in sahiwal cattle: Insights into the patterns in the natural course of infection

Mastitis is a global concern in the dairy sector, demanding innovative solutions for effective management for quality lifetime milk production. In this study, infrared thermography (IRT) as a non-invasive technology was integrated into routine farm activities for continuous health monitoring of animals. For 30 days, we systematically monitored the udder health status in 40 Sahiwal cows (160 quarters), employing IRT along with the California Mastitis Test (CMT). We also assessed somatic cell count (SCC), microbial identification, and milk quality parameters of representative samples. The thermal imaging data was analyzed, considering both backward propagation from the 0th day to the -10th day and forward propagation from the 0th day to the +10th day. Our findings revealed that on the 0th day, the mean temperatures of the udder surface skin temperature (USST) and teat skin surface temperature (TSST) exhibited differences (p < 0.05) between the quarters affected by sub-clinical mastitis (SCM) and clinical mastitis (CM) in comparison to the healthy quarters, with the highest degree of difference observed. The observed temperature differences between CM and SCM quarters compared to healthy ranged from 1.8 to 3.62 °C and 0.98 to 3.23 °C for USST, and from 1.68 to 3.16 °C and 0.56 to 2.32 °C for TSST, respectively. Furthermore, our observations indicated that both udder and teat quarters responded differently to mastitis. A temperature rise of 1.37 °C in SCM quarters and 1.75 °C in CM quarters was observed between the -10th and -8th day relative to day 0, with the increase being more pronounced in the morning hours. Also, a notable temperature surge occurred during the -2nd and -1st days relative to the 0th day. The log10SCC values and milk quality parameters significantly differed (p < 0.05) between mastitis-affected and healthy samples. In addition, Staphylococcus spp. was identified as the predominant mastitis-causing pathogen in the bacteriological identification conducted in this study. Therefore, IRT efficiently assesses the initiation point of udder infection in Sahiwal cows, aiding in effective udder health management.

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.

3D/4D printed super reconstructed foods: Characteristics, research progress, and prospects

Super reconstructed foods (SRFs) have characteristics beyond those of real system in terms of nutrition, texture, appearance, and other properties. As 3D/4D food printing technology continues to be improved in recent years, this layered manufacturing/additive manufacturing preparation technology based on food reconstruction has made it possible to continuously develop large-scale manufacture of SRFs. Compared with the traditional reconstructed foods, SRFs prepared using 3D/4D printing technologies are discussed comprehensively in this review. To meet the requirements of customers in terms of nutrition or other characteristics, multi-processing technologies are being combined with 3D/4D printing. Aspects of printing inks, product quality parameters, and recent progress in SRFs based on 3D/4D printing are assessed systematically and discussed critically. The potential for 3D/4D printed SRFs and the need for further research and developments in this area are presented and discussed critically. In addition to the natural materials which were initially suitable for 3D/4D printing, other derivative components have already been applied, which include hydrogels, polysaccharide-based materials, protein-based materials, and smart materials with distinctive characteristics. SRFs based on 3D/4D printing can retain the characteristics of deconstruction and reconstruction while also exhibiting quality parameters beyond those of the original material systems, such as variable rheological properties, on-demand texture, essential printability, improved microstructure, improved nutrition, and more appealing appearance. SRFs with 3D/4D printing are already widely used in foods such as simulated foods, staple foods, fermented foods, foods for people with special dietary needs, and foods made from food processingbyproducts.

Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0

Food safety is closely related to human health. However, the regulation and testing processes for food safety are intricate and resource-intensive. Therefore, it is necessary to address food safety risks using a combination of deep learning, the Internet of Things, smartphones, quick response codes, smart packaging, and other smart technologies. Intelligent designs that combine digital systems and advanced functionalities with biosensors hold great promise for revolutionizing current food safety practices. This review introduces the concept of Food Safety 4.0, and discusses the impact of intelligent biosensors, which offer attractive smarter solutions, including real-time monitoring, predictive analytics, enhanced traceability, and consumer empowerment, helping improve risk management and ensure the highest standards of food safety.

Why Acute Undernutrition? A Qualitative Exploration of Food Preferences, Perceptions and Factors Underlying Diet in Adolescent Girls in Rural Communities in Nigeria

BACKGROUND

Adolescent girls are nutritionally vulnerable due to their rapid growth and increased nutrient requirements. Nigeria has the sixth-largest population in the world. This study qualitatively explored the food preferences, perceptions of nutritive value and factors underlying food consumption of adolescent girls in rural communities in Nigeria.

METHODS

The data were collected via the free listing of foods and focus group sessions conducted in the Hausa language with 48 unmarried adolescent girls. The discussions were audio-recorded, transcribed, translated into English, and analyzed using a deductive thematic framework.

RESULTS

The mean age of the respondents was 13.0 ± 2.7, and almost half (48%) had a primary school education. A total of 19 and 23 foods were identified as preferred, and perceived as nourishing, respectively. The top 10 foods present on both free lists overlapped considerably in terms of cognitive salience. The focus group themes included nutrition knowledge, food preferences, autonomy, household food allocation, courtship practices, and agricultural landscapes and economic access. The participants had minimal knowledge of nutrients and food groups, and their preferred foods were limited in diversity. The key factors in food preferences were desirable health effects, sensory attributes, and the contribution of foods to a desirable body image for marriage. Household food choices depended on parents. Thus, a desire for independence was an incentive for early marriage, mostly at 13 to 17 years. Gender inequities in household food distribution (quantity) and animal protein intake were reported. The participants believed that boys need more food for strength to impregnate girls. As part of a courtship practice, the girls received gifts of animal source foods from potential suitors. The food options were limited by financial challenges and low agricultural diversity.

CONCLUSION

To interrupt the cycle of inadequate food consumption and undernutrition in these adolescent girls, policy makers need to promote nutrition education and address the underlying determinants of inequitable access to nutritious foods.