Agricultural developments have played a pivotal role in the emergence of sedentary human civilisation. The agri-food sector remains crucial globally, yet conventional farming methods persist. Information and Communication Technology (ICT), with advancements like Machine Learning (ML), Internet of Things (IoT), Big Data Analytics (BDA), and Cloud computing, have significantly transformed agriculture. These technologies aim to promote sustainability by reducing greenhouse gas emissions, water consumption, enhancing nutrition, and improving agricultural efficiency to meet the growing food demand, especially with the projected global population of 9.6 billion by 2050.

IoT has emerged as a game-changer in agriculture, enabling control over robots, autonomous vehicles, drones, and irrigation systems. It also aids in forecasting through classical and machine learning models, tracing the supply chain, and monitoring environmental, facility, machinery, weather, water, soil, and crop conditions. IoT platforms enhance sustainability, logistics, cold chain management, governance, collaboration, and risk mitigation, offering better connectivity, reduced human intervention, and improved energy management.

However, IoT faces challenges in technology, society, and organisation, including decentralisation, privacy, security, data governance, internet availability, interoperability, and managing big data. Social challenges involve societal acceptance, technical skills, and legislation, while organizational challenges include trust, initial investment, return on investment, and heterogeneity.

Despite the significance of IoT in agriculture, few studies have explored the influence of more than five critical factors on its adoption. Additionally, much of the existing literature overlooks these factors, signaling a research gap. Furthermore, previous research primarily focused on technical challenges during implementation, with limited attention to social, economic, and organizational factors affecting IoT adoption. To address these gaps, this study ‘Unlocking adoption challenges of IoT in Indian Agricultural and Food Supply Chain’ by Narwane et al aims to answer the following research questions:

• What are the critical factors of IoT adoption in the agri-food supply chain, particularly in emerging economies?
• What are the interrelationships between these critical factors?
• What are the most significant factors and how do they rank in importance?
• What steps should be taken to facilitate effective IoT adoption in the agri-food supply chain?

So, IoT offers immense potential for the agricultural sector, but its adoption is hindered by various challenges. Addressing these challenges and understanding the critical factors is essential for harnessing IoT's capabilities to ensure a sustainable and productive future for the agri-food industry, particularly in emerging economies.

IoT for agriculture and food sector

IoT in the agri-food sector encompasses various functions such as greenhouse cultivation, precision agriculture, and machinery automation. It enables controlled plant growth in greenhouses through wireless sensor networks, enhancing efficiency and reducing human intervention. Precision agriculture employs data sensing via IoT sensors to control variables like labour, equipment, crop maturity, air and soil quality, and weather. IoT-enabled machinery operates autonomously, reducing losses and improving productivity. Tracing and tracking of agri-food products using technologies like Radio Frequency Identification (RFID) provide product history visibility. IoT is also used for monitoring livestock farming, forestry, aquaponics, and crop farming.

IoT for agriculture and food supply chain

IoT in the agri-food supply chain helps measure and monitor sustainability indicators like crop productivity, fertiliser usage, and water efficiency. It enables object interconnection and data sharing across the supply chain, using technologies like Wireless Sensor Networks (WSN) and RFID. RFID is widely employed for temperature monitoring, shelf-life estimation, supply chain management, quality monitoring, livestock management, cold chain monitoring, and food product traceability. WSN is used for data collection and monitoring in food storage, transportation, and various environmental conditions. IoT technology is crucial in identifying and measuring sustainability indicators, although the dynamic nature of agriculture poses challenges that IoT can address.

Several tools and techniques have been used to understand IoT adoption for the agri-food supply chain, including artificial intelligence-based approaches, deep learning, genetic algorithms, fuzzy logic, structural equation modeling, and more. These tools are utilised for forecasting, monitoring, waste reduction, and sustainability analysis in the agri-food sector.

Discussion

The study identifies a comprehensive list of proposed critical factors categorised into Technical, Social, Economic, and Organisational factors that influence IoT implementation in the agri-food domain. Experts from industry and academia with 15-25 years of experience validated and finalised the list of factors.

These factors include capital investment costs, security concerns, lack of interoperability, network challenges, lack of standardisation, energy efficiency, technical complexity, data volume, connectivity, scalability, heterogeneity, legislation, trust, data reliability, data governance, technical skill requirements, infrastructure limitations, data management, decentralisation, environmental sustainability, user acceptance, ease of use, risk assessment, and the lack of economic analysis.

These factors encompass the challenges and considerations essential for successful IoT adoption in the agri-food sector and must be explored collectively for an effective and sustainable implementation of IoT in emerging economies. Recent technological advances in Industry 4.0 and big data analytics offer solutions to several of these challenges, making IoT a promising reality in countries like India.

The DEMATEL (Decision-Making Trial and Evaluation Laboratory) methodology was employed to determine the cause-effect relationships among the identified critical factors. The methodology was adapted from a previous study.

The DEMATEL analysis revealed that out of the 24 identified critical factors, 14 were identified as "Causal" factors, and 10 as "Effect" factors. These factors were then ranked based on their (r-c) values.

The study categorised and ranked critical factors into various categories, highlighting their significance. The top five significant factors were "Lack of Interoperability," "Environmental Sustainability," "Trust," "Lack of Security," and "Network Challenges." "Lack of Interoperability" was identified as the most critical factor, while "Heterogeneity" was the least critical factor.

A "Cause and Effect" diagram was created to visualise the causal relationships among the identified critical factors. Factors above the horizontal axis were considered "Cause" factors, while those below were "Effect" factors. Most of the "Causal" factors influenced both "Causal" and "Effect" factors, illustrating their interconnectedness.

The results of the DEMATEL method were validated by comparing them with existing literature studies from various countries, which supported the key findings of the study.

The study discussed recent implementations of IoT systems in agri-food supply chain, highlighting their benefits in monitoring, sustainability, quality control, and waste reduction.

The study's findings can be used by researchers, academicians, policymakers, and organisations for further research, policy formulation, and business model development. The critical factors identified can serve as a basis for identifying collaboration opportunities and new business models.

The paper can be accessed here