Digital Twins in Agriculture: Orchestration and Applications

Digital Twins have emerged as an outstanding opportunity for precision farming, digitally replicating in real-time the functionalities of objects and plants. A virtual replica of the crop, including key agronomic development aspects such as irrigation, optimal fertilization strategies, and pest management, can support decision-making and a step change in farm management, increasing overall sustainability and direct water, fertilizer, and pesticide savings. In this review, Digital Twin technology is critically reviewed and framed in the context of recent advances in precision agriculture and Agriculture 4.0. The review is organized for each step of agricultural lifecycle, edaphic, phytotechnologic, postharvest, and farm infrastructure, with supporting case studies demonstrating direct benefits for agriculture production and supply chain considering both benefits and limitations of such an approach. Challenges and limitations are disclosed regarding the complexity of managing such an amount of data and a multitude of (often) simultaneous operations and supports.

Unveiling Digital Transformation: A Catalyst for Enhancing Food Security and Achieving Sustainable Development Goals at the European Union Level

The digital revolution is reshaping various aspects of society, including having a profound impact on food security and the advancement of Sustainable Development Goals (SDGs). This study investigates the relationship between digital transformation, quantified through the components of the Digital Economy and Society Index (DESI), and SDGs related to food (SDG1, SDG2, SDG3, and SDG10), along with the overall SDG Index score. The data used for investigation are sourced from reports issued by the European Commission concerning DESI, as well as the SDG reports for the period from 2017 to 2022. The paper elucidates how different components of digitalization, such as connectivity, digital skills, internet usage, and digital public services, influence the attainment of food security objectives and broader sustainable development targets using structural equation modeling and cluster analysis. The findings underscore the pivotal role of digital technologies in enhancing poverty alleviation, health and well-being, and, in particular, mitigating inequality. This study contributes to understanding the complex relationship between digital transformation and food security, offering insights for policymakers, practitioners, and stakeholders aiming to leverage technology for advancing SDGs and fostering a more equitable and sustainable future.

Global Food Security and Sustainability Issues: The Road to 2030 from Nutrition and Sustainable Healthy Diets to Food Systems Change

The accomplishment of food/nutrition security for all across sustainable food systems (SFS) is tied to the Sustainable Development Goals (SDGs). SFS is connected to all SDGs via the traditional framework of social inclusion, economic development, environmental safety, inclusivity, and the development of sustainable food systems. We suggest that, for the world to achieve sustainable development, a shift to SFS is necessary to guarantee food/nutrition security for all, while operating within planetary boundaries to protect ecosystems and adapt to and mitigate climate change. Therefore, there is a requirement for original approaches that implement systemic and more participatory methods to engage with a wider range of food system stakeholders. However, the lack of skills and tools regarding novel methodologies for food system transformation is a key obstacle to the deployment of such approaches in practice. In the first part of this review, a summary of some challenges that occur in the governance of food system transformation is given. Through a case study of plant-based proteins and their biological and chemical modification as diets shift towards alternative proteins, we demonstrate that resource-efficient food systems and food waste, through system transformation, are useful in understanding both (i) how food system transformation has ensued and (ii) how the required transformation is prohibited. Finally, we discuss the implications of food system transformation in terms of nutrition and sustainable healthy diets, which are needed to achieve changes in food safety systems in the future. The linkage of food and the environment is evident, focusing on nutrition and sustainable healthy diets. This cannot be accomplished without system change and research towards new foods and, more specifically, new proteins such as plant-based ones and their biological and chemical modification.

Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications

Promoting sustainability in food and nutrition systems is essential to address the various challenges and trade-offs within the current food system. This imperative is guided by key principles and actionable steps, including enhancing productivity and efficiency, reducing waste, adopting sustainable agricultural practices, improving economic growth and livelihoods, and enhancing resilience at various levels. However, in order to change the current food consumption patterns of the world and move toward sustainable diets, as well as increase productivity in the food production chain, it is necessary to employ the findings and achievements of other sciences. These include the use of artificial intelligence-based technologies. Presented here is a narrative review of possible applications of artificial intelligence in the food production chain that could increase productivity and sustainability. In this study, the most significant roles that artificial intelligence can play in enhancing the productivity and sustainability of the food and nutrition system have been examined in terms of production, processing, distribution, and food consumption. The research revealed that artificial intelligence, a branch of computer science that uses intelligent machines to perform tasks that require human intelligence, can significantly contribute to sustainable food security. Patterns of production, transportation, supply chain, marketing, and food-related applications can all benefit from artificial intelligence. As this review of successful experiences indicates, artificial intelligence, machine learning, and big data are a boon to the goal of sustainable food security as they enable us to achieve our goals more efficiently.

Agricultural biotechnology for sustainable food security

Of late, global food security has been under threat by the coronavirus disease 2019 (COVID-19) pandemic and the recent military conflict in Eastern Europe. This article presents the objectives of the Sustainable Development Goals and the European Green Deal related to achieving food security and sustainable development in European Union (EU) agriculture, taking the aforementioned threats into account. In addition, it discusses the future of plant agricultural biotechnology and artificial intelligence (AI) systems, considering their potential for reaching the goal of food security. Paradoxically, the present challenging situation may allow politicians and stakeholders of the EU to realize opportunities and use the potential of the biotechnology sector.

Global impact of COVID-19 on agriculture: role of sustainable agriculture and digital farming

The rise and spread of the coronavirus pandemic (COVID-19) has created an imbalance in all sectors worldwide, massively disrupting the global economy. Social distancing, quarantine regulations, and strict travel restrictions have led to a major reduction in the workforce and loss of jobs across all industrial sectors. One of the sectors completely exposed was the agriculture and food sector. The initiation of a nationwide lockdown by the government resulted in the shutdown of industries globally impacting the overall supply chain from farmer to consumer. The need of the hour is to propose effective solutions which can serve the dual purpose of market growth as well as customer satisfaction. This paper reviews the impact of COVID-19 on the agro-food system and its economy stressing critical factors like food production, demand, price hikes, security, and supply chain resilience. To conserve natural resources and meet the sustainable development goals (SDG), importance has been given to adopting sustainable agricultural practices with a prime focus on techniques like urban agriculture, crop rotation, hydroponics, and family farming. Possible advancements like the use of digital tools, mainly artificial intelligence, machine learning, deep learning, and block-chain technology, in the agro-food sector have been discussed as they could be a promising tool to develop a self-reliant society. This work would be a perfect platform to understand the growing impact of the pandemic as well as supporting cost-effective solutions for a green ecosystem.

A Deep-Learning-Based Detection Approach for the Identification of Insect Species of Economic Importance

Artificial Intelligence (AI) and automation are fostering more sustainable and effective solutions for a wide spectrum of agricultural problems. Pest management is a major challenge for crop production that can benefit from machine learning techniques to detect and monitor specific pests and diseases. Traditional monitoring is labor intensive, time demanding, and expensive, while machine learning paradigms may support cost-effective crop protection decisions. However, previous studies mainly relied on morphological images of stationary or immobilized animals. Other features related to living animals behaving in the environment (e.g., walking trajectories, different postures, etc.) have been overlooked so far. In this study, we developed a detection method based on convolutional neural network (CNN) that can accurately classify in real-time two tephritid species (Ceratitis capitata and Bactrocera oleae) free to move and change their posture. Results showed a successful automatic detection (i.e., precision rate about 93%) in real-time of C. capitata and B. oleae adults using a camera sensor at a fixed height. In addition, the similar shape and movement patterns of the two insects did not interfere with the network precision. The proposed method can be extended to other pest species, needing minimal data pre-processing and similar architecture.

Can the use of digital technology improve the cow milk productivity in large dairy herds? Evidence from China's Shandong Province

Introduction

Improving milk productivity is essential for ensuring sustainable food production. However, the increasing difficulty of supervision and management, which is associated with farm size, is one of the major factors causing the inverse relationship between size and productivity. Digital technology, which has grown in popularity in recent years, can effectively substitute for manual labor and significantly improve farmers' monitoring and management capacities, potentially addressing the inverse relationship.

Methods

Based on data from a survey of farms in Shandong Province in 2020, this paper employs a two-stage least squares regression model to estimate the impact of herd size on dairy cow productivity and investigate how the adoption of digital technology has altered the impact of herd size on dairy cow productivity.

Results

According to the findings, there is a significant and negative impact of herd size on milk productivity for China's dairy farms. By accurately monitoring and identifying the time of estrus, coupled with timely insemination, digital technology can mitigate the negative impact of herd size on milk productivity per cow.

Discussion

To increase dairy cow productivity in China, the government should promote both small-scale dairy farming and focus on enhancing management capacities of farm operators, as well as large-scale dairy farms and increase the adoption of digital technologies.

Responsible robotics design-A systems approach to developing design guides for robotics in pasture-grazed dairy farming

Application of robotics and automation in pasture-grazed agriculture is in an emergent phase. Technology developers face significant challenges due to aspects such as the complex and dynamic nature of biological systems, relative cost of technology versus farm labor costs, and specific market characteristics in agriculture. Overlaying this are socio-ethical issues around technology development, and aspects of responsible research and innovation. There are numerous examples of technology being developed but not adopted in pasture-grazed farming, despite the potential benefits to farmers and/or society, highlighting a disconnect in the innovation system. In this perspective paper, we propose a "responsibility by design" approach to robotics and automation innovation, using development of batch robotic milking in pasture-grazed dairy farming as a case study. The framework we develop is used to highlight the wider considerations that technology developers and policy makers need to consider when envisaging future innovation trajectories for robotics in smart farming. These considerations include the impact on work design, worker well-being and safety, changes to farming systems, and the influences of market and regulatory constraints.

Future agricultural systems and the role of digitalization for achieving sustainability goals. A review

By leveraging a wide range of novel, data-driven technologies for agricultural production and agri-food value chains, digital agriculture presents potential enhancements to sustainability across food systems. Accordingly, digital agriculture has received considerable attention in policy in recent years, with emphasis mostly placed on the potential of digital agriculture to improve efficiency, productivity and food security, and less attention given to how digitalization may impact other principles of sustainable development, such as biodiversity conservation, soil protection, and human health, for example. Here, we review high-level policy and law in the German and European context to highlight a number of important institutional, societal, and legal preconditions for leveraging digital agriculture to achieve diverse sustainability targets. Additionally, we combine foresight analysis with our review to reflect on how future frame conditions influencing agricultural digitalization and sustainability could conceivably arise. The major points are the following: (1) some polices consider the benefits of digital agriculture, although only to a limited extent and mostly in terms of resource use efficiency; (2) law as it applies to digital agriculture is emerging but is highly fragmented; and (3) the adoption of digital agriculture and if it is used to enhance sustainability will be dependent on future data ownership regimes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13593-022-00792-6.

Applications of Digital Twin across Industries: A Review

One of the most promising technologies that is driving digitalization in several industries is Digital Twin (DT). DT refers to the digital replica or model of any physical object (physical twin). What differentiates DT from simulation and other digital or CAD models is the automatic bidirectional exchange of data between digital and physical twins in real-time. The benefits of implementing DT in any sector include reduced operational costs and time, increased productivity, better decision making, improved predictive/preventive maintenance, etc. As a result, its implementation is expected to grow exponentially in the coming decades as, with the advent of Industry 4.0, products and systems have become more intelligent, relaying on collection and storing incremental amounts of data. Connecting that data effectively to DTs can open up many new opportunities and this paper explores different industrial sectors where the implementation of DT is taking advantage of these opportunities and how these opportunities are taking the industry forward. The paper covers the applications of DT in 13 different industries including the manufacturing, agriculture, education, construction, medicine, and retail, along with the industrial use case in these industries.

ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ("big data") and the exponential increase in computing power have allowed the appearance of "new" modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine "old" and "new" modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.

Governance of Responsible AI: From Ethical Guidelines to Cooperative Policies

The increasingly pervasive role of Artificial Intelligence (AI) in our societies is radically changing the way that social interaction takes place within all fields of knowledge. The obvious opportunities in terms of accuracy, speed and originality of research are accompanied by questions about the possible risks and the consequent responsibilities involved in such a disruptive technology. In recent years, this twofold aspect has led to an increase in analyses of the ethical and political implications of AI. As a result, there has been a proliferation of documents that seek to define the strategic objectives of AI together with the ethical precautions required for its acceptable development and deployment. Although the number of documents is certainly significant, doubts remain as to whether they can effectively play a role in safeguarding democratic decision-making processes. Indeed, a common feature of the national strategies and ethical guidelines published in recent years is that they only timidly address how to integrate civil society into the selection of AI objectives. Although scholars are increasingly advocating the necessity to include civil society, it remains unclear which modalities should be selected. If both national strategies and ethics guidelines appear to be neglecting the necessary role of a democratic scrutiny for identifying challenges, objectives, strategies and the appropriate regulatory measures that such a disruptive technology should undergo, the question is then, what measures can we advocate that are able to overcome such limitations? Considering the necessity to operate holistically with AI as a social object, what theoretical framework can we adopt in order to implement a model of governance? What conceptual methodology shall we develop that is able to offer fruitful insights to governance of AI? Drawing on the insights of classical pragmatist scholars, we propose a framework of democratic experimentation based on the method of social inquiry. In this article, we first summarize some of the main points of discussion around the potential societal, ethical and political issues of AI systems. We then identify the main answers and solutions by analyzing current national strategies and ethics guidelines. After showing the theoretical and practical limits of these approaches, we outline an alternative proposal that can help strengthening the active role of society in the discussion about the role and extent of AI systems.

Business Models for the Internet of Services: State of the Art and Research Agenda

The relevance of the Internet of Services (IoS) comes from the global reach of the Internet into everyone’s home and daily activities and from the move from a manufacturing-based economy to a service-based economy. The IoS is seen as a new ecosystem where service providers and consumers explore their business networks for service provision and consumption. The scientific literature refers to IoS as an important cornerstone for Industry 4.0 and Future Internet; thus, it becomes relevant to study how IoS interacts with business models. Nevertheless, there is a lack of clarity on such an intersection. Moreover, a systematic review of IoS-based business models is still missing. This paper aims to make a systematic review of IoS-based business models and their application fields. We included studies from Scopus and Web of Science databases, we excluded duplicated papers and short conference versions of the later full paper journal publications. Twenty-three different studies are presented, categorized in the sub-areas of IoS, and then by the fields of applications. The main finding highlights the opportunities of IoS applications in different fields, offering directions for future research on this new arena.

Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm

Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.

Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.

COVID-19 pandemic and agriculture in Romania: effects on agricultural systems, compliance with restrictions and relations with authorities

Like most economic sectors, agriculture has been significantly affected by the COVID-19 pandemic. This study was designed to understand the impact of the initial stages of the pandemic on the agricultural sector in Romania. A web-based research study of farmers was conducted using an online questionnaire. Participants (n = 148) were self-selected, by answering the questionnaire online. The results highlighted that the pandemic was having an impact on agricultural costs, labor, farm management and food security. Among the farmers who were asked to describe the effects of the COVID-19 pandemic on delays with agricultural work, only 35.1% indicated that they had not registered delays. When farmers were asked if they anticipated a future increase in costs in agriculture as a result of the COVID-19 pandemic, 45.9% of respondents felt that costs would increase. Fifty-seven percent of participants reported that they would continue to apply measures to reduce the impacts of the pandemic. Our findings and analysis indicated that agricultural systems were vulnerable and that the agricultural sector must be closely monitored and supported to maintain food security in times of crisis. For food security and better resilience of agri-food systems in Romania, the study identified needs for more automation and mechanization in farms, digital solutions for the public and private sector and continuous dialogue between farmers and authorities. We suggest the pandemic can be an opportunity for the reevaluation of agricultural production systems in Romania and beyond, and for the development of more innovative strategies, sustainable practices and digital solutions in agriculture.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12571-021-01239-8.

Mycoherbicides for the Noxious Meddlesome: Can Colletotrichum be a Budding Candidate?

Invasive plant species are a major threat to biodiversity and agricultural productivity. Hence, efforts to manage these menace involves extensive and effective use of chemical herbicides amongst others. However, not only is the impact of control with chemical herbicides short-lived but also leads to negative impact on human health and environment due to non-target herbicide-drift and runoff from the sprayed areas. This has ushed in much-anticipated nature-based potential regulators of weed species, in an attempt to lower the utilisation of chemical herbicides. Mycoherbicides have been seen as a benign, eco-friendly, host-specific, and replacement for chemical herbicides. There are several noteworthy genera of fungus that have been proved to be effective against weeds. They either produce strong phytotoxins or are often used as spore/conidia-based solutions and applied as a spray in growth media. One of such potential genera is Colletotrichum Corda 1831. Compared to other potent fungal genera, with well-established roles in conferring herbicidal activities by producing competent phytotoxins, only a few species under genus Colletotrichum are known to produce fungal metabolites be used as phytotoxins. This article elucidates the current understanding of using spore suspension/phytotoxin of Colletotrichum as a weedicide. We also discuss the interaction between fungal metabolites release and Colletotrichum-target plant, from a molecular and biochemical point of view. This review article has been written to accentuate on the potency of Colletotrichum, and to serve as an eye-opener to consider this genus for further fruitful investigations. However, inconsistency associated with mycoherbicides in terms of viability and efficacy under field conditions, production of bioactive compound, slow natural dispersal ability, etc., have often reduced their utility. Hence, our study emphasizes on the need to do extensive research in elucidating more phytotoxins from necrotrophic phytopathogenic microorganisms with novel mode of action for field application.

Improving the Event-Based Classification Accuracy in Pit-Drilling Operations: An Application by Neural Networks and Median Filtering of the Acceleration Input Signal Data

Forestry is a complex economic sector which is relying on resource and process monitoring data. Most of the forest operations such as planting and harvesting are supported by the use of tools and machines, and their monitoring has been traditionally done by the use of pen-and-paper time studies. Nevertheless, modern data collection and analysis methods involving different kinds of platforms and machine learning techniques have been studied lately with the aim of easing the data management process. By their outcomes, improvements are still needed to reach a close to 100% activity recognition, which may depend on several factors such as the type of monitored process and the characteristics of the signals used as inputs. In this paper, we test, thought a case study on mechanized pit-drilling operations, the potential of digital signal processing techniques combined with Artificial Neural Networks (ANNs) in improving the event-based classification accuracy in the time domain. Signal processing was implemented by the means of median filtering of triaxial accelerometer data (window sizes of 3, 5, and up to 21 observations collected at 1 Hz) while the ANNs were subjected to the regularization hyperparameter’s tunning. An acceleration signal processed by a median filter with a window size of 3 observations and fed into an ANN set to learn and generalize by a regularization parameter of α = 0.01 has been found to be the best strategy in improving the event-based classification accuracy (improvements of 1% to 8% in classification accuracy depending on the type of event in question). Improvement of classification accuracy by signal filtering and ANN tuning may depend largely on the type of monitored process and its outcomes in terms of event duration; therefore, other monitoring applications may need particular designs of signal processing and ANN tuning.

Review: Anticipating alternative trajectories for responsible Agriculture 4.0 innovation in livestock systems

Technological change has been a constant feature of livestock systems leading to the third agricultural 'green' revolution of the mid-20th century. Digital technologies are now leading us into the fourth agricultural revolution, where sustainable food production is supported by technologies that collect data useful for farm and supply chain performance improvement, along with task automation and compliance. However, the potential benefits of digital agricultural futures are uncertain and plagued by unrealized expectations of previous innovations. The aims of this paper are to articulate current trends in technology and livestock systems and anticipate future trajectories for Agriculture 4.0 in relation to meeting sustainability and animal welfare outcomes for livestock systems. We use a 'Futures Triangle' approach to review the role of technology in livestock systems. The main findings are that previous work envisioning technological livestock futures have favoured pull of the future factors (techno-optimists) or weight of the past (techno-pessimists), rather than a balance of pull, push and weighting factors. Responsible Agriculture 4.0 innovation requires public-private collaboration of innovation system stakeholders, including policy makers, farmers, consumers, as well as technology developers, to enable development of transition pathways from a systems perspective. The use of responsible innovation processes, including anticipation on alternative futures, should also be built into innovation processes to support critical reflection on technological trajectories and related innovation system consequences, both desirable and undesirable.

Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields

Artificial Intelligence is an emerging technology in the field of agriculture. Artificial Intelligence-based tools and equipment have actually taken the agriculture sector to a different level. This new technology has improved crop production and enhanced instantaneous monitoring, processing, and collection. The most recent computerized structures using remote sensing and drones have made a significant contribution to the agro-based domain. Moreover, remote sensing has the capability to support the development of farming applications with the aim of facing this main defy, via giving cyclic records on yield status during studied periods at diverse degrees and for diverse parameters. Various hi-tech, computer-supported structures are created to determine different central factors such as plant detection, yield recognition, crop quality, and several other methods. This paper includes the techniques employed for the analysis of collected information in order to enhance the productivity, forecast eventual threats, and reduce the task load on cultivators.

Are artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective

Purpose

This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context.

Design/methodology/approach

20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of “Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used.

Findings

The study's outcome indicates that “lack of central and state regulations and rules” and “lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties.

Research limitations/implications

This study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care.

Originality/value

This study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP.

Artificial Intelligence in the Industry 4.0, and Its Impact on Poverty, Innovation, Infrastructure Development, and the Sustainable Development Goals: Lessons from Emerging Economies?

Artificial intelligence in the fourth industrial revolution is beginning to live up to its promises of delivering real value necessitated by the availability of relevant data, computational ability, and algorithms. Therefore, this study sought to investigate the influence of artificial intelligence on the attainment of Sustainable Development Goals with a direct focus on poverty reduction, goal one, industry, innovation, and infrastructure development goal 9, in emerging economies. Using content analysis, the result pointed to the fact that artificial intelligence has a strong influence on the attainment of Sustainable Development Goals particularly on poverty reduction, improvement of the certainty and reliability of infrastructure like transport making economic growth and development possible in emerging economies. The results revealed that Artificial intelligence is making poverty reduction possible through improving the collection of poverty-related data through poverty maps, revolutionizing agriculture education and the finance sector through financial inclusion. The study also discovered that AI is also assisting a lot in education, and the financial sector allowing the previously excluded individuals to be able to participate in the mainstream economy. Therefore, it is important that governments in emerging economies need to invest more in the use of AI and increase the research related to it so that the Sustainable Development Goals (SDGs) related to innovation, infrastructure development, poverty reduction are attained.

Energy as a Factor of Investment Attractiveness of Regions for Agricultural Enterprises

The aim of the article is to identify and assess the relationship between the investment attractiveness of regions for agricultural enterprises and the energy factor. Classical theories of the location of agriculture emphasise the importance of the market factor. The energy factor has so far been ignored, despite the global trend related to the increasing importance of production scales and rising energy consumption in agriculture. There are also no methodological proposals that allow a comprehensive assessment of the investment attractiveness of regions for agricultural enterprises, taking into account the leading location factors. The article presents the author’s methodological model based on the weight-correlation method of valorisation of investment attractiveness of regions for economic entities that invest in agricultural production. It contains a sub-aggregate describing the energy factor. This proposal is a contribution to the theory of the location of agriculture in the field of location factor analysis. The developed methodological model is used to explain location decisions of agricultural enterprises at the regional level. Access to energy as well as energy management increase locational advantages and reduce the economic risk of carrying out agricultural activities in economic units, which contributes to an increase in the sustainability of agricultural production. This is especially true in areas dominated in the past by state-owned and cooperative enterprises, which are the dominant group of enterprises in this area after privatization. The proposed methodology was positively verified on the example of Polish regions, as a significant influence of the energy factor on investment attractiveness at the local level was demonstrated.

A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots

The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method—an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis.

Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization

Agricultural digitalization is providing growing amounts of real-time digital data. Biophysical simulation models can help interpret these data. However, these models are subject to complex uncertainties, which has prompted calls for interdisciplinary research to better understand and communicate modelling uncertainties and their impact on decision-making. This article develops two corresponding insights from an interdisciplinary project in a New Zealand agricultural research organization. First, we expand on a recent Royal Society Open Science journal article (van der Bles et al. 2019 Royal Society Open Science 6, 181870 (doi:10.1098/rsos.181870)) and suggest a threefold conceptual framework to describe direct, indirect and contextual uncertainties associated with biophysical models. Second, we reflect on the process of developing this framework to highlight challenges to successful collaboration and the importance of a deeper engagement with interdisciplinarity. This includes resolving often unequal disciplinary standings and the need for early collaborative problem framing. We propose that both insights are complementary and informative to researchers and practitioners in the field of modelling uncertainty as well as to those interested in interdisciplinary environmental research generally. The article concludes by outlining limitations of interdisciplinary research and a shift towards transdisciplinarity that also includes non-scientists. Such a shift is crucial to holistically address uncertainties associated with biophysical modelling and to realize the full potential of agricultural digitalization.

Analysis of adoption trends of in-parlor technologies over a 10-year period for labor saving and data capture on pasture-based dairy farms

The use of precision technology is increasingly seen as an option to improve productivity, animal welfare, resource use efficiency, and workplace features on dairy farms. There is limited research related to longitudinal adoption patterns of precision dairy technologies and reasons for any patterns. The aim of this analysis was to investigate trends in technology adoption regarding both the amount (number of farms with a technology) and intensity (number of technologies per farm) of adoption. Surveys of parlor technology adoption were conducted on New Zealand dairy farms in 2008, 2013, and 2018, with 532, 500, and 500 respondents, respectively. Technologies were grouped into labor-saving (LS, such as automatic cluster removers) or data-capture (DC, such as in-line milk meters) categories. Trends were examined for farms that had only LS, only DC, or LS+DC technologies. Technology adoption increased over time; the likelihood of technology adoption in 2018 (and 2013 in parentheses) increased by 21 (22), 7 (68), and 378% (165) for LS, DC, and LS+DC technology groups, respectively, compared to 2008. Farms with LS+DC technologies also had a greater proportion of LS technologies compared to non-LS+DC farms, although this relationship declined over the 10-yr period. The use of a rotary versus herringbone parlor was estimated to be associated with 356 and 470% increase in the likelihood of adopting LS technologies and LS+DC, respectively, from 2008 to 2018. Regional differences in adoption were also found, with the likelihood of adopting DC and LS+DC technologies found to be 46 and 59% greater, respectively, in the South Island of New Zealand, compared to the base region of Waikato. The results highlight the importance of understanding spatial and temporal farm characteristics when considering future effect and adoption of precision dairy technologies. For example, the analysis indicates the occurrence of 2 trajectories to technology investment on farms, where larger farms are able to take advantage of technology opportunities, but smaller farms may be constrained by factors such as lack of economies of scale, limited capital to invest, and inability to retrofit technology into aging parlor infrastructure.