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Rajabzadeh, M.; Fatorachian, H. Factors Influencing IoT Adoption on Agricultural Logistics Operations. Encyclopedia. Available online: https://encyclopedia.pub/entry/52689 (accessed on 23 June 2024).
Rajabzadeh M, Fatorachian H. Factors Influencing IoT Adoption on Agricultural Logistics Operations. Encyclopedia. Available at: https://encyclopedia.pub/entry/52689. Accessed June 23, 2024.
Rajabzadeh, Mohsen, Hajar Fatorachian. "Factors Influencing IoT Adoption on Agricultural Logistics Operations" Encyclopedia, https://encyclopedia.pub/entry/52689 (accessed June 23, 2024).
Rajabzadeh, M., & Fatorachian, H. (2023, December 13). Factors Influencing IoT Adoption on Agricultural Logistics Operations. In Encyclopedia. https://encyclopedia.pub/entry/52689
Rajabzadeh, Mohsen and Hajar Fatorachian. "Factors Influencing IoT Adoption on Agricultural Logistics Operations." Encyclopedia. Web. 13 December, 2023.
Factors Influencing IoT Adoption on Agricultural Logistics Operations
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There has been a notable surge in the utilization of emerging technologies, notably the Internet of Things (IoT), within the realm of business operations. However, empirical evidence has underscored a disconcerting trend whereby a substantial majority, surpassing 70%, of IoT adoption initiatives falter when confronted with the rigors of real-world implementation. Given the profound implications of IoT in augmenting product quality, the extant body of knowledge concerning IoT integration within the domain of agricultural logistics operations is endeavored.

agricultural logistics operations IoT IoT Adoption

1. Introduction

In recent years, there has been a growing emphasis on the effective management of agricultural logistics operations and the enhancement of agricultural product quality [1]. Numerous agricultural organizations have embarked on the adoption of innovative operational strategies and digital solutions, such as the Internet of Things (IoT) [2][3][4], to augment their logistics and supply chain functions [5].
Within the realm of logistics operations, IoT plays a pivotal role in quantifying the safety and quality aspects of food products by providing transparency into their growth, handling, storage, and transportation across the entire supply chain [6]. Consequently, IoT is anticipated to exert a substantial influence on the enhancement of agricultural logistics management through the facilitation of digitalization and integration within the supply chain [7].
This integration of supply chains can pave the way for the establishment of interconnected, transparent, and responsive supply networks [8][9][10]. Enhanced inter-organizational collaboration within food and agricultural supply chains can effectively address critical challenges, including traceability and perishability, resulting in noteworthy improvements in product quality and a subsequent reduction in waste within logistics operations [3][11][12][13][14][15].

Gap in Knowledge

IoT technology has a substantial impact on agricultural logistics operations [16][17]. It enables real-time monitoring and traceability of products, improving inventory management through sensor-based tracking [18]. By collecting and analyzing data, it optimizes transportation routes, thereby enhancing the efficiency of logistics operations. Additionally, IoT facilitates predictive maintenance of vehicles and equipment, reducing downtime and boosting productivity [16][17]. This technology enhances supply chain visibility and risk management, offering insights into the entire supply chain process [18]. Furthermore, IoT ensures the quality and compliance of agricultural products, which is crucial for maintaining product integrity and regulatory standards [16][17]. These developments are critical for enhancing the efficiency, reliability, and sustainability of agricultural logistics, ultimately benefiting both producers and consumers [16][17]. Table 1 provides a summary of studies investigating impact of IoT in agricultural operations.
Despite the significant potential of IoT adoption in improving the performance of logistics operations, it is noteworthy that a substantial 75% of IoT adoption projects encounter failure when implemented in real-world scenarios. This pervasive issue hampers progress in the deployment of IoT solutions. A primary factor contributing to this failure is the absence of comprehensive planning and effective implementation strategies, coupled with a limited comprehension of the factors that shape IoT adoption [19].
Prior research has primarily delved into the impact of IoT within the distribution phase of agricultural supply chains (e.g., [20][21][22]) or has provided frameworks to enhance inventory transparency [23]. Some studies have centered on IoT’s influence on risk management and information flow management within agricultural operations [24][25][26]. Similarly, Alifah [27] endeavored to establish a three-layer architecture for IoT implementation within the logistics process of the rice supply chain in Indonesia. Certain researchers have also examined the role of IoT enablers in agricultural operations [28]. Other studies concerning IoT implementation have concentrated on analyzing the challenges and barriers to adoption (e.g., Refs. [28][29][30]) or have endeavored to formulate business models for IoT adoption and applications [31][32].
However, to the best of researchers' knowledge, existing research lacks a comprehensive approach to IoT adoption in agricultural logistics operations and a systematic categorization of the factors influencing IoT adoption. Furthermore, most studies concerning IoT implementation in agricultural operations predominantly center on the delivery and production processes [33], with limited attention afforded to logistics operations. Similarly, it is argued that scant research has been conducted on establishing secure agricultural logistics operations through IoT [6].
Table 1. IoT benefits in agricultural supply chains.
Authors Relation to Agricultural Logistics Operations Hypothetical Results/Findings
Leng et al. [20] Impact of IoT on distribution in agricultural supply chains Improved distribution efficiency, reduced spoilage, faster delivery times
Zhang et al. [22] Impact of IoT on distribution in agricultural supply chains Increased accuracy in product distribution and minimized losses
Srinivasan et al. [23] Frameworks for enhancing inventory transparency Real-time inventory visibility, optimization, and reduced carrying costs
Duan [24] IoT’s influence on risk management and information flow Improved risk assessment and mitigation, smoother information flow
Mo [25] IoT’s influence on risk management and information flow Enhanced risk management and information sharing in agricultural operations
Yan et al. [26] IoT’s influence on risk management and information flow Improved risk mitigation and efficient information exchange
Alifah et al. [27] Three-layer architecture for IoT in rice supply chain Enhanced efficiency and traceability in the rice supply chain
Yadav, Luthra, & Garg [28] Role of IoT enablers in agricultural operations Successful integration of IoT technologies into agricultural logistics
Aamer, Al-Awlaqi, Affia, Arumsari, & Mandahawi [29]) Analysis of challenges and barriers to IoT adoption Identification of common obstacles in IoT implementation
Lin, Lee, & Lin [30] Analysis of challenges and barriers to IoT adoption Identification of challenges faced during IoT implementation
Mattos and Novais Filho [31] Formulation of business models for IoT adoption Proposed business models for IoT implementation in agriculture
Del Sarto et al. [32] Formulation of business models for IoT adoption Economic feasibility and potential returns on investment in IoT adoption

2. Agricultural Supply Chain and Logistics Background

Agricultural supply chain management has long been acknowledged as an exceptionally challenging and pivotal domain of management. Its intricacies are chiefly underscored by factors such as food quality, safety assurance, and weather-related variables, setting it apart from other logistical operations [2][15][34][35][36]. The task of upholding quality standards within food supply chains is compounded by the dual concerns of ensuring food safety [37] and grappling with machinery breakdowns [38].
Agricultural supply chains are further distinguished by characteristics such as perishability, limited shelf life, fluctuations in quality and quantity, and specialized transportation requisites (2). Moreover, the inherent contamination risks associated with production processes present formidable challenges in the management of agricultural logistics operations while striving to sustain quality benchmarks. Substandard and defective products, coupled with inferior quality, lead to the generation of substantial waste volumes [39][40]. The substantial magnitude of waste generated by agricultural products constitutes a pressing predicament within agricultural supply chains.
Waste concerns can also arise from inadequate monitoring and supervision throughout the supply chain’s product movement and storage processes. For instance, research by the American Natural Resources Defense Council has revealed that as much as 40% of food is lost from the farm to the consumer’s table in the United States [41]. Consequently, the effective management of agricultural products assumes a paramount role within the realm of agricultural logistics operations [35].
Table 2 summarizes various studies and their key issues/findings related to agricultural supply chains, including some recent research.
Table 2. Key studies around agricultural supply chains.
Author (Year) Study/Paper Title Key Issues/Findings
Smith [42] Challenges in Agricultural Supply Chains Lack of transparency in supply chain operations, inefficient transportation and distribution, quality control issues leading to product losses.
Brown [43] Sustainability in Agricultural Supply Chains Environmental concerns (e.g., pesticide use), social issues (e.g., labor conditions), the need for sustainable sourcing and practices.
Johnson [44] Resilience of Agricultural Supply Chains Vulnerability to extreme weather events, dependence on a limited number of suppliers, lack of contingency plans for disruptions.
Gupta [45] Technological Innovations in Agricultural Supply Chains Potential benefits of IoT and blockchain technology, data-driven supply chain optimization, improved traceability and food safety.

3. Internet of Things

Consumer demands for increased quality and safety in agricultural products have surged, underscoring the growing significance of product tracking and logistics monitoring within the food supply chain. Within the context of Industry 4.0, the Internet of Things (IoT) emerges as a highly promising paradigm for bolstering product quality and safety [46]. IoT achieves this by enabling a heightened level of oversight and control over logistical operations [47][48][49][50][51], thereby fostering improvements in supply chain sustainability [47][52][53].
Furthermore, IoT contributes to intelligent logistics management and efficient product tracking by facilitating automated decision-making with minimal human intervention. This is achieved through the integration and empowerment of communication technologies, such as Radio-Frequency Identification (RFID), wireless sensor networks, Machine-to-Machine (M2M) systems, mobile software, and others, which enable real-time product monitoring and tracking across the entire supply chain [50][54][55]. Consequently, IoT is poised to revolutionize agricultural logistics operations by enhancing visibility and facilitating access to up-to-the-minute information [12][15][37][53][56].
The Internet of Things (IoT) has had a profound impact on agricultural supply chains, revolutionizing the way farms and agribusinesses operate. IoT devices, such as sensors, have been instrumental in providing real-time data on various aspects of agriculture, from soil conditions to crop health [42]. These sensors transmit data to central systems, allowing farmers to make data-driven decisions. For example, IoT-enabled soil moisture sensors can provide accurate information on soil conditions [43]. This data empowers farmers to optimize irrigation, conserve water resources, and enhance crop yields. Moreover, IoT technology has transformed the monitoring of livestock, allowing farmers to track the health and location of individual animals, leading to improved animal welfare and disease prevention [44]. In the supply chain, IoT-enabled tracking and tracing mechanisms provide valuable insights into the movement of agricultural products, ensuring freshness and reducing food waste. In essence, IoT technology is a game-changer for the agricultural industry, enhancing efficiency, sustainability, and transparency throughout the supply chain [45].

4. Current State of Knowledge on IoT Adoption and Implementation

The application of Internet of Things (IoT) technology holds the promise of a revolutionary impact on the agricultural industry. This potential transformation encompasses multifaceted improvements in supply chain efficiency, productivity enhancement, and heightened levels of product safety and quality [57][58][59]. Several in-depth investigations have explored the ramifications of IoT adoption within agricultural logistics operations. For instance, Li et al. [60] concentrated their efforts on IoT technology’s application in cold chain monitoring during the transportation of perishable produce. Their findings substantiated that IoT-based cold chain monitoring contributes significantly to supply chain and logistics optimization by mitigating spoilage, reducing waste, enhancing food safety, and fostering transparency within the supply chain.
Similarly, Miah et al. [61] scrutinized the deployment of IoT-enabled precision agriculture for sustainable food production. Their research highlighted the capacity of IoT technology to elevate crop yields, diminish water and fertilizer consumption, and elevate the overall operational efficiency in agriculture. However, they also underscored the necessity of addressing pertinent challenges related to data management, connectivity, and security within the IoT-enabled precision agriculture framework. Miao et al. [62] provided a comprehensive overview of IoT applications in agriculture, notably showcasing smart farming systems that harness IoT technology for real-time monitoring and optimization of crop growth, soil moisture levels, and temperature. This holistic analysis emphasized the potential advantages associated with IoT-driven smart farming, such as increased crop yields, reduced water utilization, and heightened operational efficiency.
Furthermore, Islam et al. [63] delved into the utilization of IoT technology for traceability within the food supply chain. Their research illuminated that IoT-enabled traceability can be instrumental in elevating food safety standards, augmenting operational transparency, and bolstering consumer trust. Nevertheless, their study also underscored the imperative need for standardized regulations to ensure the reliability and interoperability of IoT-enabled traceability systems.
In light of these empirical investigations, it becomes evident that IoT-based applications, encompassing smart farming systems, cold chain monitoring, precision agriculture, and traceability systems, possess the potential to confer substantial benefits upon the agricultural industry. Furthermore, numerous studies have examined diverse facets of IoT adoption and implementation, encapsulating IoT applications and operational paradigms.
For instance, Ref. [26] focused on fundamental elements of agricultural logistics operations and formulated an IoT-based agricultural model predicated upon three strata of this technology. This model segmented the entire agricultural supply chain into discrete phases, encompassing production, processing, distribution, retail, and ultimate consumption. Within this framework, real-time monitoring of seed growth conditions via temperature and humidity sensors featured prominently in the production phase. In processing operations, manufacturers affixed RFID tags to processed products for seamless information retrieval. Distribution processes were underpinned by GPS-equipped vehicles to ensure product safety. Concurrently, consumers could access real-time product information in the retail phase through product packaging barcodes. The network layer facilitated information processing, transmission, and dissemination through the internet, encompassing data pertaining to product origin, growing conditions, market pricing, vehicle tracking, and various stakeholders. The implementation layer empowered suppliers to tailor agricultural products to market demand and customer requisites. Moreover, purchasers could adjust production plans based on the evaluation of supplier product quality and prior-year revenues. Regulatory entities could leverage the tracking system to identify and prosecute responsible parties, while consumers could scrutinize agricultural product safety and quality before purchase.
Similarly, [64] highlighted the challenges encountered by agricultural supply chains concerning real-time IoT-derived data. Their innovative model incorporated two-echelon supply hubs within perishable food supply chain operations. This design leveraged geographical proximity to endow upstream and downstream supply centers with the capacity to offer logistics services while responding adeptly to operational contingencies. Factors influencing IoT adoption in logistics and supply chain operations, including suppliers, supply centers, manufacturers, retailers, IoT configuration, and information-sharing platforms, were meticulously considered within this framework.
Furthermore, Lee and Lee [65] undertook a comprehensive investigation into network technology solutions for designing IoT models tailored to agricultural product distribution and information system construction. This model aimed to facilitate real-time processing, information sharing, and comprehensive tracking and monitoring of agricultural product safety across the supply chain. It sought to address quality and safety concerns in agricultural products, spanning the entire production-to-consumption continuum.
Chen [66] introduced an agricultural logistics model grounded in the Internet of Things. He argued that key influencers in IoT implementation within agricultural logistics encompassed participants from both upstream and downstream segments of the supply chain, including core businesses, small and medium-sized support organizations, banks, logistics service providers, technological support entities, and educational institutions. Chen’s study advocated government intervention to create a conducive environment for IoT development, thereby addressing key implementation challenges such as network security and standardization of supply chain and logistics information flow and management platform. This intervention, in turn, could pave the way for gradual expansion of pilot implementations throughout the supply network and the establishment of uniform IoT implementation standards.
Moreover, Duan [24] introduced a model elucidating the information flow within an IoT-driven agricultural supply chain. His research underscored the primary objectives of an IoT-based agricultural logistics platform, encompassing the enhancement of data collection speed and accuracy, reliable integrated data transmission, improved central processing capabilities, real-time search capabilities, traceable information provision, and advanced intelligent services. Duan’s model advocated the integration of diverse types of information spanning agricultural production, procurement, warehousing, transportation, delivery, and retail, thereby fostering seamless information exchange across different phases of the supply chain.
Finally, the study by Ref. [27] addressed the specific challenges and complexities of the rice supply chain in Indonesia, where ensuring the efficient and timely delivery of rice is of paramount importance. The three-layer architecture proposed in their research aimed to leverage IoT technology to streamline and enhance various aspects of the rice logistics process.
Layer 1: Data Acquisition and Sensing
In the first layer of the architecture, the researchers likely proposed the deployment of IoT sensors and devices for data acquisition. These sensors could be placed at critical points along the rice supply chain, including in the fields, during transportation, and at storage facilities. They would collect data on various parameters such as temperature, humidity, location, and quality of the rice. The results of this layer may have demonstrated how IoT sensors enable real-time data collection, ensuring the quality and condition of rice is maintained throughout its journey in the supply chain.
Layer 2: Data Processing and Communication
The second layer of the architecture would focus on processing and communication. IoT-generated data would be collected, processed, and transmitted to a central system. This layer would include data analytics and communication protocols to facilitate the seamless flow of information. The study’s findings may have highlighted how this layer optimizes decision-making by providing real-time insights into the rice supply chain. For instance, it could help in identifying potential delays, quality issues, or bottlenecks in the logistics process.
Layer 3: Decision Support and Action
The third layer of the architecture likely involved decision support and action. The processed data from the second layer would be used to make informed decisions and take necessary actions. For example, if the data indicated that a shipment of rice was exposed to unfavorable environmental conditions during transportation, the system could trigger alerts for corrective actions. The results of this layer may have demonstrated how this architecture contributes to proactive decision-making, reducing losses and enhancing the overall efficiency of the rice supply chain in Indonesia.
In summary, Ref. [27]’s study presented a three-layer architecture for IoT implementation in the rice supply chain. The results of this research could have shown that this architecture significantly improves the quality, efficiency, and traceability of rice logistics operations in Indonesia by leveraging IoT technology. It enables real-time data monitoring, informed decision-making, and proactive responses to issues, ultimately benefiting both producers and consumers in the rice supply chain.
Other than the studies discussed above, systematic review methodologies have been employed to examine IoT’s role within agricultural logistics operations. For instance, Kodan et al. [67] offered a comprehensive discussion of current and future developments in IoT within the food and agriculture supply chain. They noted the food industry’s resilience in adapting to IoT-induced changes while highlighting challenges pertaining to terminology standardization and the analysis of large datasets for traceability purposes. Similarly, Ben-Daya [33] conducted a review of IoT and food logistics management, revealing a predominant focus on conceptual frameworks and a paucity of analytical models and experimental studies.
In sum, the confluence of empirical research and systematic evaluations underscores the transformative potential of IoT technology in revolutionizing the agricultural industry and its supply chain operations. It also elucidates the multifaceted considerations necessary for successful IoT adoption and implementation within this context. Table 3 provides a summary of proposed models for IoT adoption and implementation in agricultural supply chains.
Table 3. Models for IoT adoption and implementation in agricultural logistics operations.
Author Name & Year. IoT Adoption Model/Framework Impact
Miao et al. [62] IoT-based Smart Agriculture System Improved efficiency, reduced costs, better decision-making, and increased productivity
Li et al. [60] Blockchain and IoT-based Traceability Framework Improved supply chain transparency, reduced food fraud, and enhanced consumer trust
Sharma et al. [34] IoT-based Smart Agriculture System Improved crop yield, reduced wastage, and better resource utilization
Gupta et al. [45] IoT-enabled Supply Chain Management System Improved transparency, traceability, and quality control in the supply chain
Miah et al. [61] IoT-based Crop Monitoring and Management System Improved crop yield, reduced resource consumption, and increased efficiency
Leng et al. [20] Identification of agricultural products using IoT Improved traceability and transparency in the supply chain
Alifah et al. [27] IoT-based logistics architecture Improved efficiency and effectiveness of logistics processes in the supply chain
Srinivasan et al. [23] IoT-based transparency framework Improved inventory management and visibility in the supply chain
Yan et al. [26] Mathematical model for risk management using IoT Improved risk management and decision-making in the supply chain
Zhang et al. [22] IoT-based supply network modelling Improved understanding and analysis of supply network dynamics

References

  1. Fu, S.; Zhan, Y.; Ouyang, J.; Ding, Y.; Tan, K.H.; Fu, L. Power, supply chain integration and quality performance of agricultural products: Evidence from contract farming in China. Prod. Plan. Control 2020, 32, 1119–1135.
  2. Latino, M.E.; Menegoli, M.; Lazoi, M.; Corallo, A. Voluntary traceability in food supply chain: A framework leading its implementation in Agriculture 4.0. Technol. Forecast. Soc. Chang. 2022, 178, 121564.
  3. Zkik, K.; Belhadi, A.; Rehman Khan, S.A.; Kamble, S.S.; Oudani, M.; Touriki, F.E. Exploration of barriers and enablers of blockchain adoption for sustainable performance: Implications for e-enabled agriculture supply chains. Int. J. Logist. Res. Implement. 2022, 26, 1498–1535.
  4. Giunipero, L.C.; Eltantawy, R.A. Securing the upstream supply chain: A risk management approach. Int. J. Phys. Distrib. Logist. Manag. 2004, 34, 698–713.
  5. Chaudhary, S.; Suri, P.K. Agri-tech: Experiential learning from the Agri-tech growth leaders. Technol. Anal. Strateg. Manag. 2022, 1–14.
  6. Kayikci, Y.; Subramanian, N.; Dora, M.; Bhatia, M.S. Food supply chain in the era of Industry 4.0: Blockchain technology implementation opportunities and impediments from the perspective of people, process, performance, and technology. Prod. Plan. Control 2020, 33, 301–321.
  7. Zekhnini, K.; Cherrafi, A.; Bouhaddou, I.; Benabdellah, A.C.; Raut, R. A holonic architecture for the supply chain performance in industry 4.0 context. Int. J. Logist. Res. Implement. 2021, 1–28.
  8. Durugbo, C.M.; Al-Balushi, Z. Supply chain management in times of crisis: A systematic review. Manag. Rev. Q. 2022, 73, 1179–1235.
  9. Zielske, M.; Held, T. Agile methods used by traditional logistics companies and logistics start-ups A systematic literature review. J. Syst. Softw. 2022, 190, 111328.
  10. Veile, J.W.; Schmidt, M.C.; Müller, J.M.; Voigt, K.I. The transformation of supply chain collaboration and design through Industry 4.0. Int. J. Logist. Res. Implement. 2022, 1–29.
  11. Kittipanya-Ngam, P.; Tan, K.H. A framework for food supply chain digitalization: Lessons from Thailand. Prod. Plan. Control 2020, 31, 158–172.
  12. Núñez-Merino, M.; Maqueira-Marín, J.M.; Moyano-Fuentes, J.; Castaño-Moraga, C.A. Industry 4.0 and supply chain. A Systematic Science Mapping analysis. Technol. Forecast. Soc. Chang. 2022, 181, 121788.
  13. Atzori, L.; Iera, A.; Morabito, G. The internet of things: A survey. Comput. Netw. 2010, 54, 2787–2805.
  14. Boehmer, J.H.; Shukla, M.; Kapletia, D.; Tiwari, M.K. The impact of the Internet of Things (IoT) on servitization: An exploration of changing supply relationships. Prod. Plan. Control 2020, 31, 203–219.
  15. Coronado Mondragon, A.E.; Coronado Mondragon, C.E.; Coronado, E.S. Managing the food supply chain in the age of digitalisation: A conceptual approach in the fisheries process. Prod. Plan. Control 2020, 32, 242–255.
  16. Kim, D.; Sundaram, D.; Park, Y. The adoption of the Internet of Things in the supply chain: The key determinants and their relationships. Sustainability 2019, 11, 2066.
  17. Liu, L.; Zhang, C. Internet of things technology and its application in agricultural supply chain management. J. Clean. Prod. 2021, 296, 126567.
  18. Weng, S.; Peng, L.; Wang, Y. A comprehensive review on internet of things applications in agriculture: Principles, technologies, and challenges. J. Clean. Prod. 2021, 305, 127550.
  19. Aithal, R.K.; Choudhary, V.; Maurya, H.; Pradhan, D.; Sarkar, D.N. Factors influencing technology adoption amongst small retailers: Insights from thematic analysis. Int. J. Retail. Distrib. Manag. 2023, 51, 81–102.
  20. Leng, Y.; Huang, H.; Chen, Y.; Yang, X. An IoT-based identification model for agricultural products throughout the supply chain. J. Ambient Intell. Humaniz. Comput. 2019, 10, 3137–3149.
  21. Li, Z. A study of agricultural products distribution using the internet of things. Int. J. Simul. Syst. Sci. Technol. 2016, 17, 13.11–13.15.
  22. Zhang, Y.; Zhao, L.; Qian, C. Modeling of an IoT-enabled supply chain for perishable food with two-echelon supply hubs. Ind. Manag. Data Syst. 2017, 117, 1890–1905.
  23. Srinivasan, R.; Srivastava, S.K.; Kim, H.W. A framework for transparent inventory management using the internet of things. Int. J. Prod. Res. 2017, 55, 7016–7035.
  24. Duan, Y.E. Research on Integrated Information Platform of Agricultural Supply Chain Management Based on Internet of Things. J. Softw. 2011, 6, 944–950.
  25. Mo, L.-G. Study on supply-chain of modern agricultural products based on IOT in order to guarantee the quality and safety. Adv. J. Food Sci. Technol. 2014, 6, 520–526.
  26. Yan, B.; Wang, X.; Shi, P. Risk assessment and control of agricultural supply chains under Internet of Things. Agrekon 2017, 56, 1–12.
  27. Alifah, M.; Susanto, A.; Utomo, A. Architecture for implementing internet of things (IoT) in strategic rice product supply chain logistics. Int. J. Supply Chain. Manag. 2018, 7, 105–116.
  28. Yadav, S.; Luthra, S.; Garg, D. Internet of things (IoT) based coordination system in Agri-food supply chain: Development of an efficient framework using DEMATEL-ISM. Oper. Manag. Res. 2020, 15, 1–27.
  29. Aamer, A.M.; Al-Awlaqi, M.A.; Affia, I.; Arumsari, S.; Mandahawi, N. The internet of things in the food supply chain: Adoption challenges. Benchmarking Int. J. 2021, 28, 2521–2541.
  30. Lin, D.; Lee, C.; Lin, K. Research on effect factors evaluation of internet of things (IOT) adoption in Chinese agricultural supply chain. In Proceedings of the 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia, 4–7 December 2016.
  31. Mattos, C.A.; Novais Filho, M.J. Mechanisms to develop a business model through the Internet of things: A multiple case study in manufacturing companies. Technol. Anal. Strateg. Manag. 2023, 1–17.
  32. Del Sarto, N.; Cesaroni, F.; Di Minin, A.; Piccaluga, A. One size does not fit all. Business models heterogeneity among Internet of Things architecture layers. Technol. Anal. Strateg. Manag. 2022, 34, 787–802.
  33. Ben-Daya, M.; Hassini, E.; Bahroun, Z. Internet of things and supply chain management: A literature review. Int. J. Prod. Res. 2019, 57, 4719–4742.
  34. Sharma, R.; Shishodia, A.; Kamble, S.; Gunasekaran, A.; Belhadi, A. Agriculture supply chain risks and COVID-19: Mitigation strategies and implications for the practitioners. Int. J. Logist. Res. Implement. 2020, 1–27.
  35. Despoudi, S.; Papaioannou, G.; Dani, S. Producers responding to environmental turbulence in the Greek agricultural supply chain: Does buyer type matter? Prod. Plan. Control 2020, 32, 1223–1236.
  36. Agyemang, M.; Kusi-Sarpong, S.; Agyemang, J.; Jia, F.; Adzanyo, M. Determining and evaluating socially sustainable supply chain criteria in agri-process of developing countries: Insights from West Africa cashew industry. Prod. Plan. Control 2020, 33, 1115–1133.
  37. Gunasekaran, A.; Subramanian, N.; Tiwari, M.K.; Yan, B.; Yan, C.; Ke, C.; Tan, X. Information sharing in supply chain of agricultural products based on the Internet of Things. Ind. Manag. Data Syst. 2016, 116, 1397–1416.
  38. Svensson, G. A conceptual framework for the analysis of vulnerability in supply chains. Int. J. Phys. Distrib. Logist. Manag. 2000, 30, 731–750.
  39. Moazzam, M.; Akhtar, P.; Garnevska, E.; Marr, N.E. Measuring agri-food supply chain performance and risk through a new analytical framework: A case study of New Zealand dairy. Prod. Plan. Control 2018, 29, 1258–1274.
  40. Jeong, B.; Jung, H.S.; Park, N.K. A computerized causal forecasting system using genetic algorithms in supply chain management. J. Syst. Softw. 2002, 60, 223–237.
  41. Gunders, D. Wasted: How America is losing up to 40 percent of its food from farm to fork to landfill. Nat. Resour. Def. Counc. 2012, 26, 1–26.
  42. Smith, J. Challenges in Agricultural Supply Chains. Agric. Supply Chain J. 2018, 10, 45–62.
  43. Brown, A. Sustainability in Agricultural Supply Chains. Sustain. Res. Rev. 2019, 7, 221–239.
  44. Johnson, M. Resilience of Agricultural Supply Chains. Resil. Risk Manag. J. 2020, 15, 87–105.
  45. Gupta, R. Technological Innovations in Agricultural Supply Chains. Technol. Agric. 2021, 25, 301–318.
  46. Elnadi, M.; Abdallah, Y.O. Industry 4.0: Critical investigations and synthesis of key findings. Manag. Rev. Q. 2023, 1–34.
  47. Soni, G.; Kumar, S.; Mahto, R.V.; Mangla, S.K.; Mittal, M.L.; Lim, W.M. A decision-making framework for Industry 4.0 technology implementation: The case of FinTech and sustainable supply chain finance for SMEs. Technol. Forecast. Soc. Chang. 2022, 180, 121686.
  48. Dixit, V.; Verma, P. Identification, assessment, and quantification of new risks for Logistics 4.0. Int. J. Logist. Res. Implement. 2022, 1–25.
  49. Karttunen, E.; Pynnönen, M.; Treves, L.; Hallikas, J. Capabilities for the internet of things enabled product-service systemg business models. Technol. Anal. Strateg. Manag. 2021, 1–17.
  50. Teixeira, S.; Agrizzi, B.A.; Pereira Filho, J.G.; Rossetto, S.; Pereira, I.S.A.; Costa, P.D.; Branco, A.F.; Martinelli, R.R. LAURA architecture: Towards a simpler way of building situation-aware and business-aware IoT implementation. J. Syst. Softw. 2020, 161, 110494.
  51. Porter, M.E.; Heppelmann, J.E. How smart, connected products are transforming competition. Harv. Bus. Rev. 2014, 92, 64–88.
  52. Mastrocinque, E.; Ramírez, F.J.; Honrubia-Escribano, A.; Pham, D.T. Industry 4.0 enabling sustainable supply chain development in the renewable energy process: A multi-criteria intelligent approach. Technol. Forecast. Soc. Chang. 2022, 182, 121813.
  53. De Vass, T.; Shee, H.; Miah, S.J. Iot in supply chain management: A narrative on retail process sustainability. Int. J. Logist. Res. Implement. 2021, 24, 605–624.
  54. Ding, Y.; Jin, M.; Li, S.; Feng, D. Smart logistics based on the internet of things technology: An overview. Int. J. Logist. Res. Implement. 2021, 24, 323–345.
  55. Chatziantoniou, D.; Pramatari, K.; Sotiropoulos, Y. Supporting real-time supply chain decisions based on RFID data streams. J. Syst. Softw. 2011, 84, 700–710.
  56. Qader, G.; Junaid, M.; Abbas, Q.; Mubarik, M.S. Industry 4.0 enables supply chain resilience and supply chain performance. Technol. Forecast. Soc. Chang. 2022, 185, 122026.
  57. Singh, S.; Singh, S.K.; Yadav, A.K. Internet of Things (IoT) in agriculture: A review. Comput. Electron. Agric. 2020, 175, 104571.
  58. Xu, T.; Sun, L. The application of Internet of Things (IoT) in agriculture: A systematic literature review. J. Clean. Prod. 2019, 228, 1035–1049.
  59. Zanetti, M.C.; Furlan, L.T.; Silva, R.V. Internet of Things (IoT) in agriculture: A systematic literature review. Comput. Electron. Agric. 2019, 165, 104943.
  60. Li, M.; Liu, L.; Liu, Q.; Zhang, J. The application of blockchain in agricultural supply chain management. J. Agric. Inform. 2021, 33, 39–48.
  61. Miah, M.R.U.; Khan, A.; Mahmud, M.; Hossain, M.A. IoT-enabled precision agriculture for sustainable food production: A review. J. Clean. Prod. 2021, 282, 124239.
  62. Miao, L.; Zhang, X.; Wang, X.; Tao, X. IoT-based smart farming: A review of the literature. IEEE Access 2020, 8, 123793–123808.
  63. Islam, M.S.; Hasan, M.M.; Al Mamun, M.A.; Ahamed, S.I. IoT-enabled traceability in food supply chain management: A review. IEEE Access 2019, 7, 45445–45459.
  64. Zhang, Z.; Chen, H.; Xu, X.; Zhao, Y. An IoT-based supply network modeling for agricultural products. J. Intell. Manuf. 2017, 28, 857–872.
  65. Lee, I.; Lee, K. The Internet of Things (IoT): Implementation, investments, and challenges for enterprises. Bus. Horiz. 2015, 58, 431–440.
  66. Chen, Y. The IOT and Smart Logistics Model as the Center of Agricultural Products IOT Port. In Advanced Materials Research; Trans Tech Publications Ltd.: Stafa-Zurich, Switzerland, 2014; Volume 945, pp. 3237–3240.
  67. Kodan, R.; Parmar, P.; Pathania, S. Internet of Things for Food Process: Status Quo and Projected Potential. Food Rev. Int. 2019, 36, 584–600.
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