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The domains of Industry 4.0 and Smart Farming encompass the application of digitization, automation, and data-driven decision-making principles to revolutionize conventional sectors. The intersection of these two fields has numerous opportunities for industry, society, science, technology and research. Relatively, this intersection is new, and still, many grey areas need to be identified. This research is a step toward identifying research areas and current trends.

The present study examines prevailing research patterns and prospective research prospects within Industry 4.0 and Smart Farming. This is accomplished by utilizing the Latent Dirichlet Allocation (LDA) methodology applied to the data procured from the Scopus database.

By examining the available literature extensively, the researchers have successfully discovered and developed three separate research questions. The questions mentioned above were afterward examined with great attention to detail after using LDA on the dataset. The paper highlights a notable finding on the lack of existing scholarly research in the examined combined field. The existing database consists of a restricted collection of 51 scholarly papers. Nevertheless, the forthcoming terrain harbors immense possibilities for exploration and offers a plethora of prospects for additional investigation and cerebral evaluation.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

The paper proposes that the incorporation of Industry 4.0 technology into agricultural operations can enhance efficiency, production and sustainability. Furthermore, it highlights the significance of creating user-friendly solutions specifically tailored for farmers and companies. The study indicates that the implementation of supportive legislative frameworks, incentive programmes and resource conservation methods might encourage the adoption of smart agricultural technologies, resulting in the adoption of more sustainable practices.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

Based on a thorough examination of existing literature, it has been established that there is a lack of research specifically focusing on the convergence of Industry 4.0 and Smart Farming. However, notable progress has been achieved in the field of seclusion. To date, the provided dataset has not been subjected to analysis using the LDA technique by any researcher.

This study aims to find how trade development and digitalization affect smart-green production. Four factors are investigated in these effects (certification, technology innovation, natural resource management, low pesticides).

The mix-method approach was employed from validating the measurement scale to test the proposed hypotheses. At first, the grounded theory is the most authoritative and standard research method in qualitative research. Secondly, quantitative analysis was employed to draw conclusions about the impact of digitalization and trade development on smart-green agricultural production.

The results found that digitalization and trade development impact the development of smart-green agricultural production through certification employment, technology innovation, and a decrease in pesticide usage. Moreover, digitalization and trade development also indirectly affect the development of intelligent green agricultural production. Meanwhile, digitalization has a higher impact than trade development.

This research is based on the premise that digitalization and trade development can drive smart green agricultural production. Still, some studies have found a deviation between trade development and environmental protection. Hence, future research can explore the incentive effect of trade development and digitalization on other industries. Second, the measurement of the dependent variables in this study is based on the premise that smart-green agricultural production has not been widely promoted, so the changes in production before and after the whole public participation in smart-green output have yet to be reflected.

Smart green production in agriculture is essential for a transition economy and the world to meet food security and protect the environment. However, the effects of certification, technology innovation, natural resource management, and low pesticides on smart-green agriculture production have yet to be identified. Insights from this study can help governments, policy-makers, and farmers in emerging economies by adapting their strategies within their local contexts.

This study is intended to introduce and summarise Industry 4.0 practices in BRICS nations (the abbreviation “BRICS” is made up of the first letters of the member countries: Brazil, Russia, India, China and South Africa) and determine each nation’s current contribution to Industry 4.0 practice implementation based on past literature. As the BRICS countries continue to play an essential role in the global economy, it is significant to understand Industry 4.0, focussing on these emerging economies.

To assess the present research work on Industry 4.0 practices and research studies in BRICS nations, a systematic literature review (SLR) is performed using the articles available on the SCOPUS database. This study is a descriptive analysis based on the frequency and year of publications, the most influential universities, most influential journals and most influential articles. Similarly, this study consists of category analysis based on multi-criteria decision-making (MCDM) methods, research design used, research method utilised, different data analysis techniques and different Industry 4.0 technologies were used to solve different applications in the BRICS nations.

According to the analysis of past literature, the primary identified practices are centred on operations productivity, waste management, energy reduction and sustainable processes. It also found that despite the abundance of research on Industry 4.0, the major academic journal publications are restricted to a small number of industries and issues in which the manufacturing and automotive industries are front runners. The categorisation of selected papers based on the year of publication demonstrates that the number of publications has been rising. It is also found that China and India, out of the BRICS countries, have contributed significantly to Industry 4.0-related publications by contributing 61 percent of the total articles identified. Similarly, this study identified that qualitative research design is the most adopted framework for research, and empirical triangulation is the least adopted framework in this field. The categorisation of selected articles facilitates the identification of numerous gaps, such as that 67.14% of the literature research is qualitative.

Understanding Industry 4.0 in the BRICS nations helps to identify opportunities for international collaboration and future cooperation possibilities. This study helps to promote collaboration between BRICS countries and other nations, organisations or businesses interested in capitalising on these growing economies' assets and capabilities related to Industry 4.0 technologies. This study helps to provide essential insights into the economic, technological and societal impacts, allowing for effective decision-making and strategic planning for a sustainable and competitive future. So, this contribution links the entire world in terms of the better utilisation of resources, the reduction of downtime, improving product quality, personalised products and the development of human resource capabilities through the application of cutting-edge technologies for nearly half of the world’s population.

In this study, BRICS nations are selected due to their significant impact on the world regarding social, economic and environmental contributions. In the current review, 423 articles published up to August 2022 were selected from the SCOPUS database. The comparison analysis of each BRICS nation in the form of applications of Industry 4.0, the primary area of focus, leading industry working, industry involvement with universities and the area that needs attention are discussed. To the best of our knowledge, this is the most recent SLR and meta-analysis study about Industry 4.0 in BRICS nations, which analysed the past available literature in nine different descriptive and category-wise classifications, considering a total of 423 articles. Based on this SLR, this study makes some important recommendations and future directions that will help achieve social, economic and environmental sustainability in BRICS nations.

This paper aims to provide an overview of the application of blockchain to agri-food supply chains, including key issues and trends. It examines the state of the art and conceptual structure of the field and proposes an agenda to guide future research.

This article performs a bibliometric analysis using VOSviewer software on a sample of 205 articles from the WoS database to identify research trend topics.

The number of publications in this area has increased since 2020, which shows a growing research interest. The research hotspots are related to the integration of blockchain technology in the agri-food supply chain for traceability, coordination between all actors involved, transparency of operations and improvement of food safety. Furthermore, this is linked to sustainability and the achievement of the sustainable development gtoals (SDGs), while addressing key challenges in the implementation of blockchain-based technologies in the agri-food supply chain.

The application of blockchain in the agri-food supply chain may consider four key aspects. Firstly, the implementation of blockchain can improve the traceability of food products. Secondly, this technology supports sustainability issues and could avoid disruptions in the agri-food supply chain. Third, blockchain improves food quality and safety control throughout the supply chain. Fourthly, the findings show that regulation is needed to improve trust between stakeholders.

The paper provides a comprehensive overview of the blockchain phenomenon in the agri-food supply chain by optimising the search criteria. Moreover, it serves to bridge to future research by identifying gaps in the field.

Managing weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and health

We propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presented

Weed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actions

The proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize crop

The proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers.

This study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature

African agricultural mechanization could lead to a mechanization divide, where only large farms have access to machines. Technological solutions such as scale-appropriate machines and institutional solutions like service markets offer hope for more inclusive mechanization. Two-wheel tractor-based service markets combine both technological and institutional elements, but there is limited research on their economic viability and challenges.

We analyze the economic viability of two-wheel tractor-based service provision based on data from service providers in Ethiopia, Burkina Faso, and Zimbabwe. We also examine the institutional framework conditions for such service providers based on qualitative interviews with these service providers and stakeholders such as machinery dealers, spare parts providers, and banks.

Two-wheel tractor-based service provision is economically highly viable, largely due to multifunctionality. Post-production services such as threshing and transportation are particularly lucrative. However, the emergence and economic sustainability of service providers can be undermined by bottlenecks such as access to finance, knowledge and skills development, access to fuel and spare parts, and infrastructure problems.

This is the first study on the economics of two-wheel tractor-based service provider models. Past studies have focused on large four-wheel tractors, but two-wheel tractors are different in many aspects, including regarding investment costs, repair and maintenance costs, capacity, and multifunctionality.

This study aims to investigate the impact of consumer ethnocentrism on consumers' evaluation of blockchain-based traceability information. It also examined how the use of quick response (QR) codes for traceability affects consumers' evaluation of traceable food products.

An online choice experiment was conducted to determine consumers' evaluation of the blockchain-based traceability of Feta cheese with a quota sample of 715 Greek consumers. Pearson bivariate correlation and mean comparison were used to examine the relationship between consumer ethnocentrism and QR use behaviour. Random parameter logit models were employed to examine consumers’ valuation of the examined attributes and interaction terms.

The results show that ethnocentric consumers are willing to pay more for blockchain-based traceability information. Ethnocentric consumers tend to scan QR codes with traceability information. Spending more time reading traceability information embedded in QR codes does not lead to a higher willingness-to-pay (WTP) for traceable food products.

The findings suggest that patriotic marketing messages can draw consumers' attention to blockchain-based traceability information. The modest WTP for and low familiarity with blockchain-based traceability systems raise the need for educating consumers regarding the benefits of blockchain in traceability systems.

This is the first study to provide timely empirical evidence of a positive WTP for blockchain-based traceability information for a processed dairy product. This study is the first to attempt to distinguish the effects of the intention to scan QR codes and reading information embedded in QR codes on consumers’ valuation of food attributes.

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.

Globalization of agriculture via the evergreen revolution (which encompasses large-scale production-collection-cleaning-processing-packaging-transportation-storage-distribution-sale of high-value cereals-fruits-flowers-vegetables-agrofuel-feedstock through technology-intensive global value chains) has opened the door to corporate capital involvement in agriculture. While the mainstream perspectives and international organizations have optimistically viewed this as a catalyst for inclusive growth, this article seeks to unveil the concealed hegemony of capital underlying the ostensibly beneficial façade of the evergreen revolution. It underscores the concerns regarding the immiseration of asset-poor farmers, petty nonfarm entrepreneurs and labourers resulting from the globalization of agriculture. Furthermore, it explores the implications for micro and macro food security in this context.

The agriculture sector is crucial for all economies, especially the developing ones. However, agricultural production is influenced by government intervention, which outshines the significant role of good governance indicators in agricultural productivity. In addition to this, the major climate changes also posed various challenges and led to water shortages and yield losses. Thus affecting agricultural production. In this paper, we address the issue by determining the association between state governance and agricultural productivity in N-11 countries.

Panel data have been collected from 2000 to 2021 through the Governance Indicator, World Development Indicator and World Bank databases. For data analysis, the researcher has utilized the autoregressive distributed lag (ARDL) estimations.

Through ARDL estimations, it is suggested that corruption (CC), employment in agriculture (EAG), political stability and violence absence (PS), rule of law (RL), regulatory equality (RQ) and water quality (WQ) significantly impact agricultural productivity (AGP) in the long run. In the short run, the impact of RL on AGP has been significant.

This study follows the method of data collection from secondary sources, which hinders the effectiveness of this study as, on the basis of the respective data, the potential of the researcher to get specific answers to research questions has been affected. Also, this study examines the context of N-11 countries from 2000 to 2021, which exerts a geographical limitation. While exploring the association between state governance and agricultural productivity, this study neglects the internal aspects of implementing state policies in firms.

On practical grounds, the significant association demonstrated by this study encourages agricultural firms to keenly consider state policies to gain sustainable agricultural development. Moreover, this study encourages agricultural firms to efficiently follow governance policies for efficient productivity. The outcomes of the study have shown that agricultural employment and governance infrastructure can efficiently enhance agricultural productivity. Besides, as per the results, water quality also positively impacts agricultural productivity; thus, relevant steps can be taken by the agricultural sector to improve the quality of water.