AI and automation in agriculture will only take hold if they prove profitable

The digital transformation of the agricultural sector requires technology to demonstrate real utility and proven economic profitability in order to achieve widespread adoption. Experts agree that, although digital infrastructure exists, the lack of high-quality, structured data constitutes the main barrier, underscoring the urgent need to continue advancing in pre-AI digitalization before implementing artificial intelligence. The true value of data lies in its aggregation and cleansing through data spaces, rather than in isolated information from individual producers. This fosters the creation of data cooperatives and mediation between data generators and users, driving a more competitive market. However, this model requires ensuring confidentiality and establishing clear boundaries, guaranteeing that trust and governance are fundamental pillars for operational integration. The validation of predictive models must take place under real-world conditions, not just in laboratories, always prioritizing human judgment in decision-making. Success will depend on professional training and solutions tailored to each crop, ruling out rigid approaches. In the realm of open data, this article highlights the urgency of standardizing and sharing agricultural information to train robust algorithms, demonstrating that open and collaborative data are the indispensable engine for sustainable, technology-driven agriculture.

Source: lavozdegalicia.es
Published on 2026-11-06

AI and automation in agriculture will only take hold if they prove profitable

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