## 4 Future Directions and Emerging Trends  
The intersection of blockchain with agriculture is evolving rapidly. Ongoing research and pilot programmes suggest several future directions that could further enhance product identity and traceability in supply chains:  
* Hybrid and scalable blockchain models: To overcome current technical limitations, more hybrid architectures that combine the strengths of different systems are expected. For example, hybrid blockchains (part public, part private) can allow a level of openness for generic data while keeping sensitive details in a restricted environment [59]. This can achieve both transparency and privacy by design. Side-chains or layer-2 networks might be employed for handling large volumes of IoT data, anchoring critical summaries or hashes back to the main chain for integrity. There is also exploration into more energy-efficient and scalable consensus mechanisms (moving beyond Proof-of-Work to Proof-of-Stake or even DAG-based ledgers) to make blockchain systems faster and more suitable for real-time supply chain operations. These innovations aim to ensure that as adoption grows, the performance and cost of using blockchain will meet industry needs. * AI and analytical integration: The combination of blockchain with Artificial Intelligence (AI) and machine learning is a promising frontier. Once supply chain data are reliably captured on blockchain, AI can be applied to this trusted dataset to extract insights and optimise operations. For instance, machine learning models could analyse blockchain-logged data on crop conditions and logistics to predict risks (like spoilage \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Barrier Category** & **Description** & **Severity** & **Frequency** \\ \hline \multirow{2}{*}{Technical scalability} & Limitations in transaction throughput, & \multirow{2}{*}{High} & \multirow{2}{*}{High} \\  & data storage, and system performance & & \\ \hline \multirow{2}{*}{Interoperability challenges} & Difficulty integrating blockchain with & \multirow{2}{*}{High} & \multirow{2}{*}{High} \\  & legacy systems and across platforms & & \\ \hline \multirow{2}{*}{Data privacy and confidentiality} & Concerns over exposing sensitive & \multirow{2}{*}{High} & \multirow{2}{*}{Medium} \\  & business information on shared ledgers & & \\ \hline \multirow{2}{*}{Economic costs and ROI} & High initial costs, unclear immediate & \multirow{2}{*}{Medium–High} & \multirow{2}{*}{High} \\  & financial benefits for small producers & & \\ \hline \multirow{2}{*}{Regulatory uncertainty} & Lack of clear legal frameworks for smart & \multirow{2}{*}{Medium–High} & \multirow{2}{*}{Medium} \\  & contracts and cross-border traceability & & \\ \hline \multirow{2}{*}{Organisational resistance} & Reluctance to change, lack of blockchain & \multirow{2}{*}{Medium} & \multirow{2}{*}{Medium} \\  & expertise, and stakeholder misalignment & & \\ \hline \multirow{2}{*}{Digital infrastructure gaps} & Limited internet connectivity and IoT & \multirow{2}{*}{Medium} & \multirow{2}{*}{Low–medium} \\  & adoption, especially in rural areas & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The main adoption barriers for blockchain in agricultural supply chains. or delays) and automatically adjust plans. Some projects [60], such as _Bext360_, are already using AI-driven devices to grade agricultural products (coffee bean quality) and then tokenising those products on blockchain for immediate payment and tracking. In the future, AI could verify data consistency on the blockchain (spotting anomalies that might indicate fraud or error) or even manage smart contracts (e.g., adaptive pricing contracts triggered by market AI forecasts). Additionally, predictive analytics on supply chain blockchains could improve food safety--AI might detect a pattern in sensor data indicating contamination and trigger preventative actions earlier. The synergy of AI with blockchain's secure data can thus lead to smarter, self-correcting supply chains that improve efficiency and reduce waste.