Shaping the Deep Tech Revolution in Agriculture 2025

Agri deep-tech opportunities for optimization TABLE 2 PillarRelevance to agri deep techOpportunities for optimization Policy and regulationsUncertainty in privacy laws and data management frameworks relating to agricultural data is a barrier to the development of agri deep tech. For several domains, such as CRISPR or nanotechnologies, regulatory frameworks need to be agile to meet the fast pace of technological evolution. Regulations should include frameworks for data governance and data sharing along with guidelines on data ownership and security. Incentives for data exchange between the government and private sector can support data aggregation that may enable several deep-tech use cases. Regulatory sandboxes and controlled research environments could be established to create safe spaces for testing with reduced regulatory burdens. Clear liability frameworks for damages arising from use-case adoption are critical to ensure that pilots are de-risked. Transparent and predictable regulatory approval pathways for market entry could be established, streamlining processes and providing predictable timelines for reviews. This can reduce the time from lab to market for promising use cases. Policy incentives such as subsidies or grants to farmers and their collectives alongside preferential procurement could be considered for improving the ability to pay and subsequent adoption of use cases. Finance and investmentsAgri deep tech requires patient capital due to long ideation and R&D cycles. However, post R&D, agri deep-tech use cases typically demonstrate long- term market potential. Such capital requirements are not typically aligned to the shorter investment horizons of traditional venture capital and hence agri deep tech requires innovative approaches to investments.Innovation challenges bundled with grants could be deployed to facilitate the conceptualization of promising ideas. De-risking instruments and proof-of-concept funds that blend different finance forms such as result-based grants and concessional loans could be implemented to reduce the financial risk of pilots. As an ecosystem matures, long-term patient capital vehicles could be established to provide flexible, long-term financing for agri deep-tech ventures that normally have longer gestation periods. An agri deep-tech impact measurement fund could be established to build evidence on deep-tech efficacy and attract impact investors. Venture capital fund capabilities could also be developed to underwrite technology risk more accurately. Human capital Agri deep-tech development demands interdisciplinary expertise combining technology and agricultural knowledge. On the demand side, adoption of agri deep tech relies significantly on last-mile digital literacy, indicating that there is an opportunity for capacity-building.The academic curriculum could integrate cross-functional domains such as agriculture and technology to ensure practical and applied learning. Additionally, industrial exposure initiatives for academic institutions along with internships, apprenticeships and industry visits could enable practical thinking. A readily accessible registry of non-domain subject-matter experts (e.g. legal, intellectual property and others) could be created to offset the costs of hiring experts in full-time roles. Training extension agents and farmer champions to act as feedback or data-collection channels could enable continuous technology refinement. Data and digital infrastructureThe training of agri deep- tech models requires large volumes of field data and powerful computational infrastructure. At the same time, good-quality data from different seasons and locations is critical for reducing the margin of error before deployment.Seamless access to high-quality datasets (e.g. soil, weather, pests) through curated and centralized diverse agricultural data from various sources could be provided to enable product development. Access to shared high-performance computing infrastructure for researchers and start-ups could be set up to accelerate conceptualization and R&D. Open repositories of training data and benchmark datasets could be made available. Innovation support Given the novel nature of most agri deep-tech use cases, a strong support ecosystem could accelerate their development. Besides finance, innovators need assistance in areas such as mentorship, market access, research and technical sandboxes. Innovation could be supported by establishing interdisciplinary deep-tech research hubs within agricultural universities. These hubs can function as centres of excellence equipped with state-of-the-art facilities. Demonstration farms for farmers and experience centres for agribusinesses could aid the commercialization of high-potential technologies. Facilitating cross-country technology transfer models and global collaboration in agri deep tech through international partnerships, joint ventures and global events could enable market access. Technical advisory for innovators could include guidance on business model re- engineering to unlock additional revenue sources. Local organizations capable of supporting geographical contextualization of agri deep-tech use cases could be set up to help adapt use cases to local agro-ecological contexts. Industry–academia collaborative action–research programmes could help uncover solutions to complex agricultural problems. In such collaborations, academia and research provide the cutting-edge foundational knowledge, while industry provides the practical expertise, market access and resources needed to bring innovations to scale, creating symbiotic value. _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Shaping the Deep-Tech Revolution in Agriculture 35