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Recent advances in artificial intelligence towards the sustainable future of agri-food industry
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Artificial intelligence has the potential to alter the agricultural and food processing industries, with significant ramifications for sustainability and global food security. The integration of artificial intelligence in agriculture has witnessed a significant uptick in recent years. Therefore, comprehensive understanding of these techniques is ne...
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... Simultaneously, there has been a big increase in the use of artificial intelligence (AI) technologies for nutrition in recent years with the potential to help food science and nutrition experts develop and promote sustainable, environmentally friendly [9,10], and ultimately personalized diets [11][12][13][14][15]. To date, there is some evidence for the utilization of AI in nutrition programs. ...
... The promotion of dietary choices that are characterized by fewer animal products and more plant-based foods while decreasing food waste [111] is important. Given the advances in the use of AI-based technologies in agri-food systems [9,10], scaling personalized nutrition to a population-wide level may reduce waste, optimize health, and promote sustainability, inducing significant benefits in the food sector. ...
Background/Objectives: Personalized nutrition programs enhanced with artificial intelligence (AI)-based tools hold promising potential for the development of healthy and sustainable diets and for disease prevention. This study aimed to explore the impact of an AI-based personalized nutrition program on the gut microbiome of healthy individuals. Methods: An intervention using an AI-based mobile application for personalized nutrition was applied for six weeks. Fecal and blood samples from 29 healthy participants (females 52%, mean age 35 years) were collected at baseline and at six weeks. Gut microbiome through 16s ribosomal RNA (rRNA) amplicon sequencing, anthropometric and biochemical data were analyzed at both timepoints. Dietary assessment was performed using food frequency questionnaires. Results: A significant increase in richness (Chao1, 220.4 ± 58.5 vs. 241.5 ± 60.2, p = 0.024) and diversity (Faith’s phylogenetic diversity, 15.5 ± 3.3 vs. 17.3 ± 2.8, p = 0.0001) was found from pre- to post-intervention. Following the intervention, the relative abundance of genera associated with the reduction in cholesterol and heart disease risk (e.g., Eubacterium coprostanoligenes group and Oscillobacter) was significantly increased, while the abundance of inflammation-associated genera (e.g., Eubacterium ruminantium group and Gastranaerophilales) was decreased. Alterations in the abundance of several butyrate-producing genera were also found (e.g., increase in Faecalibacterium, decrease in Bifidobacterium). Further, a decrease in carbohydrate (272.2 ± 97.7 vs. 222.9 ± 80.5, p = 0.003) and protein (113.6 ± 38.8 vs. 98.6 ± 32.4, p = 0.011) intake, as well as a reduction in waist circumference (78.4 ± 12.1 vs. 77.2 ± 11.2, p = 0.023), was also seen. Changes in the abundance of Oscillospiraceae_UCG_002 and Lachnospiraceae_UCG_004 were positively associated with changes in olive oil intake (Rho = 0.57, p = 0.001) and levels of triglycerides (Rho = 0.56, p = 0.001). Conclusions: This study highlights the potential for an AI-based personalized nutrition program to influence the gut microbiome. More research is now needed to establish the use of gut microbiome-informed strategies for personalized nutrition.
... Future strategies will emphasize resource efficiency, waste reduction, and lower carbon footprints, aligning with the objectives of the United Nations' Sustainable Development Goals (SDGs) and the 2030 Agenda. Optimization approaches will incorporate life cycle assessment (LCA), energy efficiency metrics, and circular economy principles, enabling decision-makers to balance environmental, social, and economic considerations effectively [87]. The development of sustainability-focused optimization algorithms, capable of quantifying trade-offs and providing actionable insights, will be crucial for addressing the global challenges of climate change and resource scarcity. ...
Optimization has become an indispensable tool in the food industry, addressing critical challenges related to efficiency, sustainability, and product quality. Traditional approaches, such as one-factor-at-a-time analysis, have been supplanted by more advanced methodologies like response surface methodology (RSM), which models interactions between variables, identifies optimal operating conditions, and significantly reduces experimental requirements. However, the increasing complexity of modern food production systems has necessitated the adoption of multi-objective optimization techniques capable of balancing competing goals, such as minimizing production costs while maximizing energy efficiency and product quality. Advanced methods, including evolutionary algorithms and comprehensive modeling frameworks, enable the simultaneous optimization of multiple variables, offering robust solutions to complex challenges. In addition, artificial neural networks (ANNs) have transformed optimization practices by effectively modeling non-linear relationships within complex datasets and enhancing prediction accuracy and system adaptability. The integration of ANNs with Industry 4.0 technologies—such as the Internet of Things (IoT), big data analytics, and digital twins—has enabled real-time monitoring and optimization, further aligning production processes with sustainability and innovation goals. This paper provides a comprehensive review of the evolution of optimization methodologies in the food industry, tracing the transition from traditional univariate approaches to advanced, multi-objective techniques integrated with emerging technologies, and examining current challenges and future perspectives.
... It has been used to predict food fraud risks using interpretable AI methods like LIME, SHAP, and WIT to interpret machine learning forecasts 34 . Additionally, AI plays a crucial role in improving food hygiene and safety by facilitating continuous monitoring of environmental cleanliness, staff, and food standards, thereby enhancing overall food safety and quality standards 35 . Furthermore, AI's impact extends to transforming the agricultural and food sector, changing precision agriculture, crop monitoring, predictive analysis, streamlining supply chains, food processing, quality control, personalized nutrition, and, importantly, food safety 8 . ...
... For instance, (Goyal et al., 2023) focuses on the state of art of Artificial Intelligence frameworks as well as the advancements of evolutionary models, hybrid models, ensemble models, and a variety of optimization strategies from 2007 to 2022. The following study (Nath et al., 2024) primarily focuses on studying the deployment of AI approaches in the agrifood supply chain. It also emphasizes the use of artificial intelligence in agriculture applications like weed identification, yield optimization, pesticide use and fruit harvesting with the potential to improve major challenges and advance global food security. ...
Sustainable agriculture requires immediate innovative strategies as climate change tightens its hold on the world's water resources, also referred to as the "blue gold" resources. This systematic review based on PRISMA guidelines explores how Artificial Intelligence and Embedded Systems alter the way we use this valuable resource. Two main areas are examined: (1) reference evapotranspiration estimation by AI for precision irrigation. In this work, we review current approaches and suggest a unique categorization strategy to direct future investigations in water management policy. (2) Smart agriculture using embedded systems, without more focus on irrigation systems. We analyze their components, characteristics, and the advancements, highlighting constraints and current gaps. By examining their synergistic adoption, insights into the growth of AI and Embedded Systems within smart farming is provided. The advanced system that uses AI to estimate ET accurately and combines it with embedded technologies for real-time irrigation control is fueled by this level of knowledge. This technology has enormous potential to enable sustainable water practices.
... This study aims to fill this knowledge gap by systematically analyzing AI's role in transforming food manufacturing processes, focusing on efficiency improvements, environmental benefits, and real-world implementation challenges. Recent advancements in Industry 4.0 & 5.0 technologies, such as cyber-physical systems, Internet of Things (IoT) devices, and data-driven methodologies, have enabled significant improvements in the food supply chain (3). These innovations facilitate real-time monitoring and optimization of production processes, enhancing resource allocation, reducing waste, and improving product quality (4). ...
... However, several challenges hinder the widespread adoption of AI in food manufacturing. These include the difficulty of integrating AI into legacy systems, a shortage of skilled professionals, and ethical concerns related to data privacy and algorithmic bias (3,10). Overcoming these barriers is imperative to unlocking the full potential of AI and ensuring its transformative impact extends across the entire food manufacturing ecosystem, from production to distribution and waste management. ...
This study aims to explore the transformative role of Artificial Intelligence (AI) in food manufacturing by optimizing production, reducing waste, and enhancing sustainability. This review follows a literature review approach, synthesizing findings from peer-reviewed studies published between 2019 and 2024. A structured methodology was employed, including database searches and inclusion/exclusion criteria to assess AI applications in food manufacturing. By leveraging predictive analytics, real-time monitoring, and computer vision, AI streamlines workflows, minimizes environmental footprints, and ensures product consistency. The study examines AI-driven solutions for waste reduction through data-driven modeling and circular economy practices, aligning the industry with global sustainability goals. Additionally, it identifies key barriers to AI adoption—including infrastructure limitations, ethical concerns, and economic constraints—and proposes strategies for overcoming them. The findings highlight the necessity of cross-sector collaboration among industry stakeholders, policymakers, and technology developers to fully harness AI's potential in building a resilient and sustainable food manufacturing ecosystem.
... Fruit and vegetable shelf life extension research has always focused on highefficiency antimicrobial agents during storage because contamination due to microbes is the primary cause of rotting in fruits and vegetables (Nath et al., 2023a(Nath et al., , b, c, 2024aSharma et al., 2023). To fully capitalize on the ability of Ag nanoparticles to be uniformly spread out in the films of alginate while retaining their inherent properties of antibacterial. ...
In the context of growing populations and depleting natural resources, food waste is a serious problem that impacts public health, the surroundings, along with the economy. Microbial creatures and moisture content in food are the primary causes of food waste. There are traditional preservations techniques exist, but they have drawbacks such as high production costs, inadequate shelf lives, undesired residue, etc. Nanotechnology-related shelf-life extension strategies have the potential to make up for the drawbacks of conventional preservation techniques due to several special qualities. This chapter outlines the fundamentals and more recent, incredibly effective uses of shelf-life extension techniques related to nanotechnology in fruits and vegetables. These techniques include active packaging, detection techniques, intelligent label systems, and combined preservation strategies. Research on nanotechnology-related intelligent labeling systems and combined preservation strategies are also briefly covered in this chapter.
... Information from it proves very valuable for designing conservation action measures and sustainable natural resources management. For instance, in Google Earth Engine, the processing of satellite data, together with AI, helps to offer insights on environmental changes for making decisions by organizations and governments (Nath et al., 2024). In addition to monitoring, AI has a great role in disaster management (N. ...
... AI-driven platforms develop new foodstuffs and their recipes. In development of machine learning models that analyze consumer preferences and market trends, helps in the identification of new product opportunities and in the end facilitates innovation at companies in the wake of changing consumer demand conditions (Nath et al., 2024). ...
Artificial Intelligence (AI) and Machine Learning (ML) are rapidly changing the face of Research and Development (R&D). This chapter deals with a profound review of the current status and future trends of AI and ML in R&D. First of all, it gives an overview of huge investments and fast growth in AI, for instance, spending on AI systems worldwide is projected to reach as high as 150 billion every year by 2026. The chapter highlights some of the most remarkable achievements in AI and ML, including transformer models like GPT-3 or Google's BERT, setting new benchmarks in natural language processing, low-code/no-code platforms democratize AI. Finally, the chapter asserts that AI and ML have the potential to transform R&D while insinuating that such development should be responsible and ethical. In adopting collaborative and open approaches, the stakeholders could reap maximum benefits from AI technologies in boosting innovation and societal benefits across different industries.
... Small-scale farmers and areas with low resources should receive special attention. To address the current ethical, social, and economic implications, it is critical to form cooperative efforts involving scholars, industry participants, legislators, and civil society organizations (Nath et al., 2024). Moreover, there are additional challenges that hinder the adoption of AI technologies in farming. ...
The green revolution, which came after the industrial revolution, boosted the crop yields produced per unit of land, but it also increased the need for synthetic fertilizers and pesticides and lowered the water table and increased salinization. In order to improve farm productivity, soil fertility is crucial and for preserving soil fertility, boosting yields, and enhancing harvest quality, fertilizer is essential. The decline in the fertility of the soil is a key constraint in enhancing food production worldwide, and improper nutrient management is a significant cause of this problem. Agroecosystems will need to implement contemporary technologies in order to produce enough food and mitigate the detrimental effects of chemical fertilization on the environment. Hence, the agri‐food industry is progressively utilizing artificial intelligence (AI) to increase productivity, efficiency, and sustainability. AI uses computational models to process data and identifies patterns for predictions or decision‐making. This review emphasizes how AI technology could be used for the predictions of manure compositions for improvement of food safety and quality. We aimed to identify the role of AI and the supporting evidences of field studies to characterize the controlled combinations of fertilizers for the efficient crop production with lowest possible plant toxicity. Also, we discuss the constraints and challenges of AI in the food and agricultural sector. In conclusion, AI‐based approaches and field studies suggested that combining organic and inorganic fertilizers can synergistically improve crop growth and yield parameters.
... Microalgae have a greater potential to produce biodiesel than cotton and palm oil. Compared to other vegetable or seed oils, algal oils have a higher concentration of polyunsaturated fatty acid double bonds (Nath, Mishra, et al., 2024;Sharma et al., 2024;Yadav et al., 2021). A glyceride molecule to form TAGs, which make up algae oil, esterifies three moles of fatty acids. ...
One of the significant parts in food processing industry is enzyme. This is due to reasons of having inadequate substrate specificity, increased produce, and eco-friendly. They boost the health benefits and deliciousness of components and finished foods. Food processing enzymes are used as food additives to change the properties of food. The application of biotechnology has been around the longest and is most widespread in the food-processing sector. The improvement of currently used procedures, such as fermentation, immobilized biocatalyst methodology, additives and processing aids manufacturing, as well as the creation of novel prospects for food biotechnology, are necessary for the further development of biotechnology-based food products and processes. This chapter investigates the important aspects of numerous enzymes and their sources, as well as the different techniques of enzyme immobilization used in the sectors of food. Additionally, numerous biotechnology breakthroughs, which are used in the processing of food, are also discussed and critically evaluated in this chapter.
... One major advantage of ANN layers lies in their ability to facilitate parallel reasoning, making neural networks highly effective for prediction. Similar to a human brain, an ANN can learn and store synaptic weights, representing connections between neurons [22]. ...
... Throughout training, the network's parameters-weights and biases-interact in complex ways to develop these representations. While the opaque, "black-box" nature of ANNs often challenges precise interpretation, tools such as feature analysis, visualization, and weight interpretation offer insights into the patterns recognized by neurons within the hidden layer [22]. ...
... eters-weights and biases-interact in complex ways to develop these representa While the opaque, "black-box" nature of ANNs often challenges precise interpre tools such as feature analysis, visualization, and weight interpretation offer insigh the patterns recognized by neurons within the hidden layer [22]. ...
The current landscape of the food processing industry places a strong emphasis on improving food quality, nutritional value, and processing techniques. This focus arises from consumer demand for products that adhere to high standards of quality, sensory characteristics, and extended shelf life. The emergence of artificial intelligence (AI) and machine learning (ML) technologies is instrumental in addressing the challenges associated with variability in food processing. AI represents a promising interdisciplinary approach for enhancing performance across various sectors of the food industry. Significant advancements have been made to address challenges and facilitate growth within the food sector. This review highlights the applications of AI in agriculture and various sectors of the food industry, including bakery, beverage, dairy, food safety, fruit and vegetable industries, packaging and sorting, and the drying of fresh foods. Various strategies have been implemented across different food sectors to promote advancements in technology. Additionally, this article explores the potential for advancing 3D printing technology to enhance various aspects of the food industry, from manufacturing to service, while also outlining future perspectives.
