Full integration is not even close to a possibility in the SQUO, but innovation solves.

Agriculture: The cost of data acquisition in data-driven agriculture continues to remain very high, thereby significantly decreasing the impact of IoT and AI in increasing agricultural productivity. This situation can only improve through innovations leading to inexpensive sensing technologies, which does not appear to become a reality in the immediate future. As the partnership between the big data technology industry and the farming community progresses, the question of who holds and owns the data will remain a top priority for farmers as well the technology companies.

The use of advanced technologies in agriculture is limited to highly developed countries, while most farmers across the rest of world are struggling to survive. The advanced technologies are generally suited to the needs of large factory farms, like those present in north America or Europe. This implies that Internet-based technologies cannot emerge as influential drivers in changing the global agricultural productivity. Therefore, the success of these technologies in developing and underdeveloped countries can only improved through strong political willpower leading to governmental support to the farmers.

Social Media: Social media and personal level data are slowly becoming the new “most valuable resource” of this era. New challenges are emerging, such as the concerns over data privacy raised by consumers and businesses, cyberattacks, use of bots, and fraudulent social media accounts by anti-enterprise bodies to negatively affect the reputation of companies and promote marketing of counterfeit/fake products on the World Wide Web. Therefore, digital teams of companies must constantly innovate to proactively tackle adverse situations and handle mishaps on social platforms.

Food Supply Chain Modernization: Quantitative studies on the benefits of IoT in supply chain are yet to be carried out. IoT integration with food business processes for control of the supply chain is a challenging topic that needs further study on a case by case basis. In general, IoT implementation in the food supply chain business is being rapidly improved considering that product-level tracking using sensors was a familiar concept to this sector. The decentralized food diversion and consignment redirection based on shelf-life prediction are the new trends which are expected to grow. These developments will help to significantly reduce the food wastage. It is likely that end-to-end supply chain traceability in agri-food industry will be achieved in future via technology implementations that differ slightly from blockchain methodologies due to the involvement of several stakeholders and actors, including end consumers.

Food Quality via Spectral Data: Presently, hyperspectral cameras and UV-Vis spectrophotometers are being used more commonly in the food industry for quality monitoring purposes than ever before. Tech startups are producing consumer focussed pocket-size spectral devices considering health conscious mindset of millennials. Miniaturized and faster hyperspectral cameras are being actively designed and developed keeping the needs of the industry in mind. Currently, a pocketsize spectral sensor costs as low as $300, but can be priced at $100 000 for commercial spectrophotometers. The cost of hyperspectral cameras is prohibitively high with starting prices of approximately $20 000 and can reach several million dollars depending on the specifications. Furthermore, the databases of spectral features for foods are also evolving and far from commercial acceptability. While similar issues are being rapidly resolved for field applications in PA, the food industry is significantly lagging. Nevertheless, the demand for spectral technologies is envisaged to continue to grow for food applications. Food Safety: With the recent change in food safety regulations, focus has changed from reaction and response to prediction and prevention. The current food safety challenges require comprehensive and organized ways to address future foodborne outbreaks with gathering and examining large volumes of genetic information for early identification of food safety issues. Future food safety challenges insist stakeholders to develop better methods of tracing food supply chains protecting food and public health. Ensuring the safety of food would be key to the future of sustainable agriculture meeting high food demands of the world. Science-based decision making and the use of advanced technologies (WGS, blockchain, and digital process data logging) would play a crucial role in gathering critical information from around the world and connecting various disease and outbreak databases to enhance the food safety. The future food safety measures demand better digital innovations to make the food supply chain safer and secured, with better traceability and accountability.

Data Ownership, Privacy, and Security: The growing digital trend in agri-food space in the form of cloud computing, IoT, and big data also comes with new challenges when it comes to cybersecurity. This is because, technologies like data platforms, wireless sensor networks, RFID, GPS, and business management systems can be vulnerable to breakdown, abuse, and misuse [126]. A breach of data security could be fatal to companies in terms of loss of business or reputation. While software companies constantly release updates to their applications and data platforms, updating is a very difficult task in some cases, for example, process control software. In addition, power failure is a common issue causing outages in farm-based IT systems.

While the use of data for AI approaches is seen as highly rewarding, there also exist many concerns, issues, and unaddressed questions around data ownership and privacy to be addressed. Since there is no guarantee that leakage of data upon sharing can be overruled, companies are hesitant to participate in AI efforts. Likewise, farmers, consumers, and smaller players in business are often left in dilemma with regards to their privacy or monetary share, should the data be used for commercial benefit. With regards to data ownership, blockchain being a peer-to-peer network that allows each participant to own his data and be involved in trade could prevent data monopoly. Efforts are needed to standardize the protocols used in the blockchain technology for its mass adoption, and its integration with AI for ensuring strong data immutability, greater transparency, and enhanced security. Smart contracts for data sharing and strong algorithms for data privacy are two important ways which can ensure that data’s value is distributed without losing trust of the parties involved.