Plant phenomics:: history, present status and challenges

ABSTRACTPlant phenomics deals with the measurement of plant phenotypes associated with genetic and environmental variation in controlled environment agriculture (CEA). Encompassing a spectrum from molecular biology to ecosystem‐level studies, it employs high‐throughput phenotyping (HTP) approaches to quickly evaluate characteristics and enhance the yields of crops in smart plant facilities. HTP uses environmental parameters for accuracy, such as software sensors, as well as hyperspectral imaging for pigment data, thermal imaging for water content, and fluorescence imaging for photosynthesis rates. They provide information on growth kinetics, physiological and biochemical characteristics, and genotype–environment interaction. Artificial intelligence (AI) and machine learning (ML) are used on a large volume of phenotypic data to predict growth rates, determine the optimal time to water plants, or detect diseases, nutrient deficiencies, or pests at an early stage. The lighting used in smart plant factories is adjusted based on the specific growth phase of the plants, such as using different light intensities, spectrums, and durations for germination, vegetative growth, and flowering stages, hydroponics as the method of providing nutrients, and CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) for improving certain characteristics, such as resistance to drought. These systems enhance crop production, yields, adaptability, and input use by optimizing the environment and utilizing precision breeding techniques. Plant phenomics with AI is a combination of several disciplines, promoting the understanding of plant–environment interactions in relation to agriculture problems such as resource use, diseases, and climate change. It affects their capacity to develop crops that capture inputs, minimize chemical application, and are resilient to climate change. Phenomics is cost‐effective, reduces inputs, and contributes to more sustainable agricultural practices, being economically and environmentally sound. Altogether, plant phenomics is central to CEA due to its capacity to capitalize on phenotypic data and genetic potential within agriculture to advance sustainability and food security. Through phenomic research, the next advancements are likely to be even more revolutionary in terms of agricultural practices and food systems worldwide.

Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical process monitoring systems, industrial big data and real-time event analytics, and machine and deep learning algorithms articulate digital twin smart factories in relation to deep learning-assisted smart process planning, Internet of Things (IoT)-based real-time production logistics, and enterprise resource coordination. Robotic cooperative behaviors and 3D assembly operations in collaborative industrial environments require ambient environment monitoring and geospatial simulation tools, computer vision and spatial mapping algorithms, and generative artificial intelligence (AI) planning software. Flexible industrial and cloud computing environments necessitate sensing and actuation capabilities, cognitive data visualization and sensor fusion tools, and image recognition and computer vision technologies so as to lead to tangible business outcomes. Purpose of the article: We show that generative AI and cyber–physical manufacturing systems, fog and edge computing tools, and task scheduling and computer vision algorithms are instrumental in the interactive economics of industrial metaverse. Generative AI-based digital twin industrial metaverse develops on IoRT and production management systems, multi-sensory extended reality and simulation modeling technologies, and machine and deep learning algorithms for big data-driven decision-making and image recognition processes. Virtual simulation modeling and deep reinforcement learning tools, autonomous manufacturing and virtual equipment systems, and deep learning-based object detection and spatial computing technologies can be leveraged in networked immersive environments for industrial big data processing. Methods: Evidence appraisal checklists and citation management software deployed for justifying inclusion or exclusion reasons and data collection and analysis comprise: Abstrackr, Colandr, Covidence, EPPI Reviewer, JBI-SUMARI, Rayyan, RobotReviewer, SR Accelerator, and Systematic Review Toolbox. Findings & value added: Modal actuators and sensors, robot trajectory planning and computational intelligence tools, and generative AI and cyber–physical manufacturing systems enable scalable data computation processes in smart virtual environments. Ambient intelligence and remote big data management tools, cloud-based robotic cooperation and industrial cyber-physical systems, and environment mapping and spatial computing algorithms improve IoT-based real-time production logistics and cooperative multi-agent controls in smart networked factories. Context recognition and data acquisition tools, generative AI and cyber–physical manufacturing systems, and deep and machine learning algorithms shape smart factories in relation to virtual path lines, collision-free motion planning, and coordinated and unpredictable smart manufacturing and robotic perception tasks, increasing economic performance. This collective writing cumulates and debates upon the most recent and relevant literature on cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative AI and cyber–physical manufacturing systems in the immersive industrial metaverse by use of evidence appraisal checklists and citation management software.

The Internet of Things (IoT) gives a strong structure for connecting things to the internet to facilitate Machine to Machine (M2M) communication and data transmission through basic network protocols such as TCP/IP. IoT is growing at a fast pace, and billions of devices are now associated, with the amount expected to reach trillions in the coming years. Many fields, including the army, farming, manufacturing, healthcare, robotics, and biotechnology, are adopting IoT for advanced solutions as technology advances. This paper offers a detailed view of the current IoT paradigm, specifically proposed for robots, namely the Internet of Robotic Things (IoRT). IoRT is a collection of various developments such as Cloud Computing, Artificial Intelligence (AI), Machine Learning, and the (IoT). This paper also goes over architecture, which would be essential in the design of Multi-Role Robotic Systems for IoRT. Furthermore, includes systems underlying IoRT, as well as IoRT implementations. The paper provides the foundation for researchers to imagine the idea of IoRT and to look beyond the frame while designing and implementing IoRT-based robotic systems in real-world implementations.

Internet of Things (IoT) provides a strong platform to connect objects to the Internet for facilitating Machine to Machine (M2M) communication and transferring data using standard network protocols like TCP/IP. IoT is gaining rapidly day by day and till date, billions of devices are already connected and in the coming few years, the number can even touch trillions. With consistent advancements, lots of areas like Military, Agriculture, Industry, Healthcare, Robotics, Nanotechnology are adapting IoT for advanced solutions. The research paper proposes a comprehensive view of the new concept of IoT especially proposed for robotics i.e. Internet of Robotic Things (IoRT). IoRT is a mix of diverse technologies like Cloud Computing, Artificial Intelligence (AI), Machine Learning and Internet of Things (IoT). The paper also discusses architecture which plays a significant role in design of Multi-Role Robotic Systems for IoRT. In addition to this, enlists technologies behind IoRT, applications of IoRT and existing robotic systems based on Humanoid, Mobile, Flying and Swarm envisaged for future IoRT systems. The paper provides a strong base for researchers to envision the concept of IoRT and enable them to think out-of-the-box to design and implement IoRT based robotic systems in real-world applications.

This chapter explores the transformative potential of integrating computer vision, artificial intelligence (AI), and the Internet of Things (IoT) in healthcare. We examine the opportunities and challenges of implementing these technologies in healthcare settings, including their potential to improve patient outcomes, enhance healthcare delivery, and reduce costs. We also consider the ethical and regulatory considerations that must be addressed when deploying these technologies, including privacy concerns and issues related to data ownership and control. Finally, we outline future directions for research and development in this area, including the need for interdisciplinary collaboration between computer science, healthcare, and regulatory experts to realize the full potential of these transformative technologies. Computer vision, AI, and IoT are rapidly evolving fields that have the potential to revolutionize healthcare. By leveraging these technologies, healthcare providers can collect and analyze vast amounts of data, enabling them to identify patterns and make more informed decisions about patient care. This can lead to earlier and more accurate diagnoses, more personalized treatment plans, and better patient outcomes. However, there are also significant challenges associated with the integration of these technologies in healthcare. For example, there are concerns about the accuracy and reliability of AI algorithms, as well as the potential for bias in their decision-making processes. Additionally, there are regulatory and ethical considerations related to data privacy, ownership, and control that must be addressed to ensure that these technologies are deployed in a responsible and ethical manner. Despite these challenges, the potential benefits of integrating computer vision, AI, and IoT in healthcare are too great to ignore. In the future, we can expect to see continued advances in these technologies, along with increased interdisciplinary collaboration between computer scientists, healthcare providers, and regulatory experts. By working together, we can unlock the full transformative potential of these technologies and improve healthcare outcomes for patients around the world. One promising area of application for computer vision, AI, and IoT in healthcare is remote patient monitoring. By using wearable devices and sensors, healthcare providers can collect real-time data on patient health and behavior, allowing them to detect and respond to potential issues before they become serious. This can lead to improved patient outcomes and reduced healthcare costs by avoiding unnecessary hospitalizations and emergency room visits. Another area of potential application is drug discovery and development. By using AI to analyze large datasets, researchers can identify promising drug candidates more quickly and accurately than traditional methods. This can lead to faster development of new drugs and therapies and ultimately improve patient outcomes. Overall, the integration of computer vision, AI, and IoT in healthcare holds great promise for improving patient outcomes, enhancing healthcare delivery, and reducing costs. However, realizing this potential will require continued investment in research and development, as well as careful attention to ethical and regulatory considerations. By working together, researchers, healthcare providers, and policymakers can ensure that these transformative technologies are deployed in a responsible and effective manner.

Developing the competence of vocational students in the field of Artificial Intelligence (AI) is a critical need to face industrial transformation 4.0. This research aims to implement a training kegiatan pelatihan on Computer Vision and Internet of Things (IoT) based on Edge Computing at SMK Negeri 9 Bandar Lampung. The kegiatan pelatihan was implemented for 6 months involving 30 students of class XII. Computer Vision is a technology that enables computers to understand and process visual information from images or videos, such as object, face, or motion recognition. Edge Computing-based Internet of Things (IoT) refers to a network of interconnected devices that process data locally (at the edge of the network) to reduce latency and cloud load. The implementation method used a mixed method approach through the stages of preparation, implementation, and evaluation. The training focused on developing practical skills using low-cost microcontroller devices for AI implementation in industry. The results showed a significant increase in student competence, with an average post-test score increase of 82% compared to the pre-test. A total of 85% of participants successfully developed AI implementation projects that are applicable to industry. The main challenges in implementation included limited infrastructure and variations in participants' basic skills. The kegiatan pelatihan produced a learning module that can be replicated in similar institutions, as well as an edge computing implementation model for AI learning at the vocational secondary level. The research contributes to the development of affordable practical AI learning methods for vocational education institutions. Abstrak Pengembangan kompetensi siswa SMK dalam bidang Artificial Intelligence (AI) menjadi kebutuhan kritis menghadapi transformasi industri 4.0. Penelitian ini bertujuan mengimplementasikan kegiatan pelatihan pelatihan Computer Vision dan Internet of Things (IoT) berbasis Edge Computing di SMK Negeri 9 Bandar Lampung. Kegiatan pelatihan dilaksanakan selama 6 bulan dengan melibatkan 30 siswa kelas XII. Computer Vision adalah teknologi yang memungkinkan komputer untuk memahami dan memproses informasi visual dari gambar atau video, seperti pengenalan objek, wajah, atau gerakan. Internet of Things (IoT) berbasis Edge Computing mengacu pada jaringan perangkat yang saling terhubung dan memproses data secara lokal (di tepi jaringan) untuk mengurangi latensi dan beban cloud. Metode pelaksanaan menggunakan pendekatan mixed method melalui tahapan persiapan, implementasi, dan evaluasi. Pelatihan berfokus pada pengembangan kemampuan praktis menggunakan perangkat mikrokontroler berbiaya rendah untuk implementasi AI dalam industri. Hasil penelitian menunjukkan peningkatan signifikan pada kompetensi siswa, dengan rata-rata kenaikan nilai post-test sebesar 82% dibanding pre-test. Sebanyak 85% peserta berhasil mengembangkan proyek implementasi AI yang aplikatif untuk industri. Tantangan utama dalam pelaksanaan meliputi keterbatasan infrastruktur dan variasi kemampuan dasar peserta. Kegiatan pelatihan ini menghasilkan modul pembelajaran yang dapat direplikasi di institusi sejenis, serta model implementasi edge computing untuk pembelajaran AI di tingkat menengah kejuruan. Penelitian memberikan kontribusi pada pengembangan metode pembelajaran AI praktis yang terjangkau untuk institusi pendidikan kejuruan.

Our theme for September, food and health, covers an enormous range of food science and nutrition topics including links between diet and noncommunicable ‘lifestyle’ diseases, personalised nutrition for specific groups within a population, the effects of epigenetics (changes caused by modification of gene expression rather than alteration of the genetic code itself) on diet and health, the influence of gut microorganisms on digestion and the provision of nutritious, healthy food products. Obesity in the UK is rising at an alarming rate and government measures adopted so far to encourage calorie reduction have failed to make an impact on this epidemic (p28). Recent figures show that childhood obesity has reached the highest point since records began (p4) and this is of particular concern as the younger the age of the child when he or she becomes overweight, the higher the risk of future obesity. Obesity represents a major threat to public health and a huge cost to the National Health Service. New government initiatives are planned to try to derail this upward trend. Groups within society sometimes have a specific need for dietary supplementation, for example, the elderly often require a specialised diet in terms of energy and protein intake and vitamin supplementation (p23). We are likely to see increasing trends towards personalised nutrition as genetic profiling becomes more common and diets are tailored to the needs of an individual. The Internet of Things (the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data) is allowing the development of apps to support and measure individual diets. Food manufacturers are working to develop products that are lower in sugar, salt and fat and to reduce portion size (p32). At the same time, they need to address the sustainability of their products, which creates additional challenges in terms of ingredients sourcing, for example animal vs plant protein. The rise of vegetarianism and veganism has prompted new research into plant sources of key proteins and vitamins (p5). Improving the diet and health of the nation will depend on a wide range of strategies being adopted simultaneously. While food manufacturers and food service outlets can reduce calories and enhance the nutritious value of their products, education and ‘nudge’ behaviour will be needed to influence people's dietary choices and food intake. email mb@biophase.co.uk Levels of severe obesity in children aged 10 to 11 years have reached the highest point since records began, according to new figures published in July 2018 by Public Health England (PHE)[1]. This trend has been decades in the making – reversing it will not happen overnight. Analysis of the National Child Measurement Programme (NCMP) between 2006 to 2007 and 2016 to 2017 details trends in severe obesity for the first time. The programme captures the height and weight of over 1m children in Reception (aged 4 to 5 years) and Year 6 (aged 10 to 11 years) in school each year. The findings also show stark health inequalities continue to widen. The prevalence of excess weight, obesity, overweight and severe obesity are higher in the most deprived areas compared to the least deprived – this is happening at a faster rate in Year 6 than Reception. The rise in severe obesity and widening health inequalities highlight why bold measures are needed to tackle this threat to children's health. The Department of Health and Social Care recently announced the second chapter of its Childhood Obesity Plan to help halve childhood obesity by 2030. Main actions include mandatory calorie labelling on menus and restrictions on price promotions on foods high in fat, salt or sugar. These measures will go out for consultation later in 2018. PHE is also working with the food industry to cut 20% of sugar from everyday products by 2020, and 20% of calories by 2024. It aims to help families to make healthier choices through its Change4Life campaigns – the free Food Scanner app reveals the sugar, fat, salt and calories in popular foods and drinks. Unhealthy weight in childhood can result in bullying, stigma and low self-esteem. It is also likely to continue into adulthood, increasing the risk of preventable illnesses including type 2 diabetes, heart disease and some cancers. ■ The Food Standards Agency has successfully completed a pilot using blockchain technology in a cattle slaughterhouse[2]. It is believed to be the first time blockchain has been used as a regulatory tool to ensure compliance in the food sector. A block chain is a type of database that takes a number of records and puts them in a block (rather like collating them on to a single sheet of paper). Each block is then ‘chained’ to the next block, using an encrypted signature. This allows block chains to be used like a ledger, which can be shared and checked by anyone with the appropriate permission. In this pilot both the FSA and the slaughterhouse had permission to access data, giving the benefit of improved transparency across the food supply chain. A further pilot took place in July that allowed farmers to access data about animals from their farm. Further work is planned to replicate this in other plants and ensure that all those across the supply chain get the full benefit of the new way data is managed and accessed as ‘permissioned’ data to the FSA, slaughterhouse and farmer. The approach has been to develop data standards with industry that will make theory reality. If the use of blockchain technology continues to show success in pilots, then the FSA believes that its permanent use would need to be industry-led because the current data model is limited to the collection and communication of inspection results. Having established a Food and Distributed Ledger Technology (DLT) collaborative group last year, the FSA continues to work with DLT experts from government, food sector, technology industry and academia on the use of blockchain, including regulatory compliance of food. ■ A new project has been launched to examine how the Internet of Things (IoT) could transform the food industry through innovations such as ‘smart’ cooking appliances, data-driven supermarket refrigeration networks and enhanced food traceability systems[3]. The project is funded by a £1.14m grant from the Engineering and Physical Sciences Research Council (EPSRC) to nurture and grow the UK's food manufacturing digital economy. The Internet of Food Things (IoFT) Network Plus will bring together data and computer scientists, chemists and economists to investigate how artificial intelligence, data analytics and emerging technologies can enhance the digitalisation of the UK food supply chain. The network, led by the University of Lincoln in partnership with the universities of Southampton, Surrey, East Anglia, and the Open University, will examine the application of the IoT in connected homes of the future – for example smart refrigerators that trigger a grocery order when food items run low, or cooking devices that could help us live healthier lives. It will also examine the traceability of food and how machine learning and artificial intelligence could be utilised to extract value from the vast amounts of data available across the whole food supply chain, improving efficiency and reducing food waste. Businesses and researchers nationally will be able to participate in workshops, run annual conferences to share best practice across the sector and bid for funding for pilot studies, projects and reviews. Collectively these initiatives, which will run until May 2021, will contribute to progressing the digitisation of food manufacturing in the UK. The aim is to specifically engage with the whole food and digital innovation chain. The project will combine interdisciplinary contributions from food science and technology practitioners, policy makers, engineers, management specialists and colleagues in social and behavioural sciences. The inclusion of food retailers like Tesco within the consortium provides access to data sets demonstrating consumer behaviours. Alongside academic expertise, the project will involve industry specialists from a range of areas, such as the global engineering company Siemens, IoT and machine management solutions’ firm IMS Evolve, supermarket chain Tesco, the rural agricultural consultancy Collison and Associates and the High Value Manufacturing Catapult. Regulators, such as the Food Standards Agency and GS1, an international agency that sets data standards for bar codes, will also have input and consumers will be engaged through representative bodies. ■ Scientists at the University of Kent have discovered that the vitamin content of some plants can be improved to make vegetarian and vegan diets more complete. Vitamin B12 (known as cobalamin) is an essential dietary component but vegetarians are more prone to B12 deficiency as plants neither make nor require this nutrient. A team, led by Professor Martin Warren at the University's School of Biosciences, has proved that common garden cress can take up cobalamin. The amount of B12 absorbed by garden cress is dependent on the amount present in the growth medium. The observation that certain plants are able to absorb B12 is important as such nutrient-enriched plants could help overcome dietary limitations in countries, such as India, which have a high proportion of vegetarians. It may also be significant as a way to address the global challenge of providing a nutrient-complete vegetarian diet, a valuable development as the world becomes increasingly meat-free due to population expansion. Researchers worked with teachers and pupils at Sir Roger Manwood's School in Sandwich, who grew garden cress in media containing increasing concentrations of vitamin B12. After seven days growth, the leaves from the seedlings were removed, washed and analysed. Vitamin B12 is unique among the vitamins because it is made only by certain bacteria and therefore has to undergo a journey to make its way into more complex multi-cellular organisms. The research highlights how this journey can be followed using the fluorescent B12 molecules, which can also be used to help understand why some people are more prone to B12-deficiency. The research is published in the journal Cell Chemical Biology[4]. ■ Campden BRI has developed a new method to rate the chilli heat of complex products, such as ready meals and cooking sauces[5]. The calibrated method uses the company's highly-trained panel of taste testers to provide retailers and manufacturers with a consistent way to rate their products as mild, medium, hot or very hot. Ingredients and even the colour and texture of a product will influence the perception of hotness. Campden BRI's method takes these factors into account and is reported to provide a consistent and reliable heat rating for food products. Samples are evaluated individually in sensory booths under coloured light to mask any differences in the colour of the products. Three major retailers, Marks and Spencer, Tesco and Sainsbury's, have joined forces with plastic packaging manufacturer, Faerch Plast, and recycling and waste management company, Viridor, to put recycled black plastic into new food grade packaging[6, 7]. The aim is to provide a circular economy solution to a previously challenging material that was difficult to recycle and to reduce the amount of virgin plastic entering the economy. The solution developed at the Viridor recycling facility enables accurate detection of the black pigment in packaging items, such as food trays, which have previously been hard-to-recycle, and separates them for shredding, melting and re-use in new packaging. Initially 120 tonnes of black plastic (8m items) will be recycled in the UK each month starting from July 2018. The volume of material will be steadily increased over the next 18 months with Viridor's specialist plastics recycling facility at Rochester in Kent becoming a centre of excellence for the initiative. The black plastic from household mixed waste recycling will be recycled into high quality mixed coloured ‘jazz’ flakes to create food grade packaging. The flakes and pellets will be taken to Faerch Plast's manufacturing facility in Ely, Cambridgeshire, where they will be used in new packaging solutions. The key to the project was the collaboration across the supply chain, with the retailers creating the sustained demand for the recycled material and recycled plastic packaging. The supermarkets all started to use the recycled black plastic for their own brand products from July. However, more work is needed to achieve high and sustainable levels of tray recycling with further investment required in commercially viable waste collection systems and sorting and recycling facilities for PET pots, tubs and trays. All the partners are signatories of WRAP's UK Plastics Pact, which aims to achieve a 20% reduction in food and drink waste across the UK and a 20% reduction in greenhouse gas emissions from food and drink consumed in the UK. The Pact sets out clear ambitions for a more responsible and resource-efficient approach to plastics by all sectors and provides the framework for collaborative action. ■ The British Poultry Council (BPC) has released its 2018 Antibiotic Stewardship Report[8], which highlights the achievements made by the British poultry meat sector's drive to deliver responsible use of antibiotics to safeguard the efficacy of antibiotics across the supply chain. The poultry meat sector became the first UK livestock sector to pioneer a data collection mechanism and share antibiotic usage data with the Government's Veterinary Medicines Directorate (VMD). Data collected by the BPC is published every year as part of the UK-Veterinary Antimicrobial Resistance and Sales Surveillance (UK-VARSS) Report. The BPC collects and monitors usage of all antibiotic classes in the UK poultry meat industry aiming to promote and apply best practice throughout the supply chain. It has facilitated sharing of best practice on responsible use of antibiotics with other livestock sectors in the UK and across the world. The BPC is working with animal and human health experts to develop a methodology for rapid on-farm diagnostics to increase speed of antibiotic sensitivity testing and to ensure early diagnosis. The aim is to use the diagnostic and sensitivity testing tools used in human medicine to better map bird health and welfare, evaluate the impact of disease control programmes and implement robust surveillance. The BPC is also supporting scientific research into examining the link between antibiotic use and resistance in the poultry production chain, understanding patterns of transmission and tackling antimicrobial resistance. The BPC claims that UK poultry farmers and veterinarians need antibiotics in their toolbox to preserve the health and welfare of the birds. It argues that responsible use of antibiotics is about more than reduction targets and that zero use is neither ethical nor sustainable as it goes against farmers’ duty to alleviate pain and suffering. A team of scientists lead by Brunel University London are developing a molecular test and a smartphone app that, when used together, can detect six key pathogens in poultry[9]. Backed by £615,000 from the UK government's Newton Fund, Brunel will work with the University of Surrey and Lancaster University to develop the tests over the next three years. The new hand-held device and smartphone app will be tested by farmers in the Philippines but could subsequently be rolled out to farmers in other developing countries. It should help farmers act fast before disease can spread and potentially infect people. It also cuts out the need to send samples away for expensive laboratory tests. Farmers in the Philippines will collect samples from their birds using a largematchbox-sized instrument that screens the DNA and RNA. The device connects wirelessly to the app to display the results, which can also feed into a central store to help track outbreaks across the islands. The whole process takes less than an hour. Near-patient molecular diagnostics have been very important in improving human health, but such technology in animal health on farms is less advanced. Hundreds of thousands of people in the Philippines and other poorer countries make a living farming poultry, so disease outbreaks can devastate their economies. DIARY 12 September 2018 NEWTRITION X – INNOVATION SUMMIT PERSONALISED NUTRITION Venue Luebeck, Germany Web https://foodregio.de/en/event-calender?vid=134 18-21 September 2018 EFSA CONFERENCE – SCIENCE, FOOD, SOCIETY Venue Parma, Italy Web https://conference.efsa.europa.eu/ 2 October 2018 NPD AND INNOVATION SUMMIT – MAKING FOOD BETTER Venue Coventry, UK Web foodbevinnovation.com/ 4-6 October 2018 21ST INTERNATIONAL CONFERENCE ON FOOD TECHNOLOGY & PROCESSING Venue London, UK Web https://foodtechnology.insightconferences.com 23-27 October 2018 IUFOST INDIA 2018, 19TH WORLD CONGRESS ON FOOD SCIENCE & TECHNOLOGY Venue Mumbai, India Web iufost2018.com/index.php 12-13 November 2018 INTERNATIONAL CONFERENCE ON AGRICULTURAL ENGINEERING AND FOOD SECURITY Venue Frankfurt, Germany Web https://agri-foodsecurity.agriconferences.com/ 20-22 November 2018 FOOD MATTERS LIVE Venue ExCeL, London Web foodmatterslive.com/ 21-22 November EHEDG WORLD CONGRESS ON HYGIENIC ENGINEERING & DESIGN Venue ExCeL, London Web ehedg-congress.org/home/ 27-29 November 2018 HI EUROPE AND NI Venue Frankfurt, Germany Web figlobal.com/hieurope/

Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection. This perspective paper synthesizes current advances, identifies major barriers, and proposes future directions to realize the transformative potential of AI‐enabled plant phenomics. We first provide an overview of AI technologies with the potential to address key challenges in phenomics, from data collection to phenotypic trait extraction and environmental sensing. We then present three case studies focusing on specialty crops (blueberry [ Vaccinium corymbosum L.] mechanical harvestability traits, strawberry [ Fragaria × ananassa (Duchesne ex Weston)] production, and citrus [ Citrus L.] disease) to illustrate practical applications of AI‐driven phenomics. Moreover, we highlight future perspectives and opportunities for further research and innovation. These include large foundation models, real‐time inference on edge devices, explainable AI, generative AI and digital twins, AI‐enhanced multi‐omics, agentic AI, and knowledge‐guided and data‐driven hybrid approaches. Finally, we discuss key challenges and limitations of applying AI to plant phenomics, including data curation, model generalization and bias, and ethical considerations related to equitable access to AI tools.

Usually, IoT devices are designed to perform specific tasks, while robots have to adapt to unpredictable situations. Artificial intelligence and machine learning help these robots cope with the emerging unexpected conditions. The Internet of Robotic Things is an evolving concept that brings together all-encompassing sensors and devices with robotic and autonomous systems. Both IoT devices and robots rely on sensors to understand the surrounding environment, to process data quickly, and to decide how to respond. Nevertheless, while most IoT systems can handle only well-defined tasks, robots can handle expected situations as well. The Internet of Robotic Things is a better Internet of Things (IoT) solution due to its ability to bridge the gap between IT and real operations. The IoRT may face several challenges in power consumption and network bandwidth limitation due to the growth in the information dissemination over the Robotic network. The main objective of this paper is to introduce an overview of the concepts and challenges in the Internet of Robotic Things based on Fog Computing technique. Further, a framework of Fog based IoRT is introduced.

The Internet of Things (IoT) concept is evolving rapidly and influencing new developments in various application domains, such as the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), Autonomous System of Things (ASoT), Internet of Autonomous Things (IoAT), Internet of Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc. that are progressing/advancing by using IoT technology. The IoT influence represents new development and deployment challenges in different areas such as seamless platform integration, context based cognitive network integration, new mobile sensor/actuator network paradigms, things identification (addressing, naming in IoT) and dynamic things discoverability and many others. The IoRT represents new convergence challenges and their need 98to be addressed, in one side the programmability and the communication of multiple heterogeneous mobile/autonomous/robotic things for cooperating, their coordination, configuration, exchange of information, security, safety and protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrency require new ideas for integrating the intelligent “devices”, collaborative robots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing, self-repair of resources, changing resource state, (re-) configuration and context based IoT systems for service implementation and integration with IoT network service composition are of paramount importance when new “cognitive devices” are becoming active participants in IoT applications. This chapter aims to be an overview of the IoRT concept, technologies, architectures and applications and to provide a comprehensive coverage of future challenges, developments and applications.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Agriculture plays a vital role in food production, resource utilization, and employment but faces challenges from population growth, climate change, and food shortages. The development of information technology has significantly contributed to the industry's development, and modern technologies such as artificial intelligence, the Internet of Things, computer vision, and machine learning have revolutionized agricultural practices. The purpose of this review is to explore the adoption of digital technologies in agriculture, with a specific focus on their application in livestock breeding. Through the examination of current literature and the utilization of various research methods, this review contributes to the existing knowledge in the field. It is established that the latest information tools allow collecting, analysing data, automating tasks and supporting decision-making, which leads to increased agricultural efficiency, resource management and sustainable development. It has been proven that modern technologies play a crucial role in increasing agricultural production, improving the efficiency of livestock and crop production. These technologies include devices and sensors, data analytics and decision support systems, as well as systems for overall farm productivity assessment. Precision technologies in agriculture, thanks to automation, sensors and machine learning, allow farmers to monitor animal health, optimise feed consumption, detect diseases at early stages and increase overall productivity. IT solutions in agriculture facilitate data processing, visualisation and decision-making, leading to lower costs, greater efficiency and improved food security. The study provides practical insights for farmers and other agricultural stakeholders who can benefit from accurate information, real-time monitoring and automated processes through the integration of modern technologies, ultimately improving agricultural practices and sustainability

The development of computer technology has revolutionized how people live and interact in society. The Internet of Things (IoT) has enabled the development of the Internet of Medical Things (IoMT) to transform healthcare delivery. Artificial intelligence has been used to improve the IoMT. Despite the significance of bibliometric analysis in a research area, to the best of the authors' knowledge, based on searches conducted in academic databases, no bibliometric analysis on artificial intelligence (AI) for the IoMT has been conducted. To address this gap, this study proposes performing a comprehensive bibliometric analysis of AI applications in the IoMT. A bibliometric analysis of top literature sources, main disciplines, countries, prolific authors, trending topics, authorship, citations, author-keywords, and co-keywords was conducted. In addition, the structural development of AI in the IoMT highlights its growing popularity. This study found that security and privacy issues are serious concerns hindering the massive adoption of the IoMT. Future research directions on the IoMT, including perspectives on artificial general intelligence, generative artificial intelligence, and explainable artificial intelligence, have been outlined and discussed.

Distributed Ledger Technologies (DLTs), including but not limited to Blockchain, have become key enablers of digital transformation across sectors. Their core attributes — immutability, traceability, decentralization, and tamper resistance — make them well-suited for secure and transparent data management. While initially linked to cryptocurrencies, DLTs are now applied in diverse domains such as identity verification, supply chain management, secure voting, and digital asset certification.In parallel, rapid advances in artificial intelligence (AI), particularly in Generative AI, are reshaping how data is generated, interpreted and applied. AI enables new forms of automation and decision-making, but it also introduces risks related to misinformation, digital fraud and security vulnerabilities. The convergence of DLT and AI offers a promising framework to address these challenges. As decentralized trust infrastructures, DLTs can enhance the transparency and integrity of AI systems by validating identities, certifying data provenance and securing sensitive assets such as contracts, medical records and intellectual property.This integration fits within the broader BIBI paradigm – Blockchain, AI, Big Data and the Internet of Things (IoT), where DLTs provide the foundation for secure, distributed coordination and auditability. Coupled with AI, they enable automated decision-making over large-scale, heterogeneous data, particularly from IoT devices, with applications in manufacturing, logistics and health care. This synergy also unlocks advanced functionalities, including decentralized identity, verifiable credentials and trusted automation, facilitating use cases such as tokenized assets and secure digital certification.Nevertheless, several technical and ethical challenges remain. The increasing computational demands of AI raise concerns about energy consumption, an issue already encountered in early DLT deployments. Energy-efficient consensus mechanisms, such as Proof-of-Stake and Byzantine Fault Tolerance, offer valuable insights for developing more sustainable AI infrastructures. Another key challenge is interoperability, as most DLT platforms operate in isolation. Enhancing interoperability would enable seamless data and asset exchange across heterogeneous ledgers, contributing to a more integrated and user-centric digital ecosystem.Looking forward, the convergence of DLT and AI holds significant potential for intelligent smart contracts, advanced traceability in critical sectors such as food safety and health care, and the development of digital twins for real-time simulation and optimization of organizational processes. This cross-disciplinary innovation can drive not only technological and economic growth but also democratic access to trusted, privacy-preserving, and sustainable digital systems.The Special Issue “Distributed Ledger Technologies and Artificial Intelligence” aims to offer a dedicated forum for researchers exploring the intersection of these two rapidly evolving fields. A total of 13 submissions were received, and following a rigorous double-blind peer review process, with at least three reviews per manuscript, three articles were selected for publication.The first article, “Energy: Reducing Latency in IoT DLTs for AI-Driven Real-Time Solutions” (Moya Perez et al., 2025), addresses the challenges of integrating IoT networks with DLT and AI, focusing on latency and energy efficiency. The authors propose Energy, a novel consensus algorithm designed for public DAG-based DLTs, which reduces or eliminates Proof-of-Work requirements. Experimental results demonstrate substantial improvements in low-payload scenarios common in IoT, enabling real-time AI model updates and efficient, secure data flow in large-scale environments.The second paper, “eFLEET: A Framework in Federated Learning for Enhanced Electric Transportation” (Ruiz de Gauna et al., 2025), presents a federated learning approach for optimizing autonomous electric vehicle routing in urban settings. Using traffic image analysis through computer vision and a DAG-based privacy layer, the framework selects optimal routes based on real-time and predictive data. The experiments show how the system improves traffic planning, enhances scalability and contributes to energy efficiency and pollution reduction in smart cities.The third contribution, “Enhancing the Viewing, Browsing and Searching of Knowledge Graphs with Virtual Properties” (Dibowski, 2024), introduces virtual properties as a user-interface enhancement for knowledge graph exploration. Defined using SHACL and evaluated via SPARQL queries, virtual properties enrich class scopes beyond direct neighbors. Successfully implemented at Bosch for over 100,000 users, this novel approach improves usability and expressiveness in knowledge-driven applications and sets a new direction for SHACL-based UI development.The authors would like to express their sincere gratitude to all the authors who submitted their contributions and to the anonymous reviewers for their thoughtful and rigorous evaluations. The authors also extend their appreciation to Professor Honghao Gao, Editor-in-Chief of IJWIS (Emerald), for his support in the publication of this special issue. The authors believe the selected papers represent cutting-edge research in this area and will be of great interest to the DLT and AI research communities.

Chapter 13 - IoIT: Integrating Artificial Intelligence With IoT to Solve Pervasive IoT Issues