AI-driven crop selection accelerates climate adaptation but risks deepening industrial monoculture dependencies and eroding biodiversity

Indigenous agricultural systems worldwide have preserved crop biodiversity through decentralized seed networks and adaptive practices, yet these are systematically marginalized by industrial models. The AI-driven selection process risks erasing centuries of indigenous knowledge by replacing it with algorithmic optimization, which often prioritizes traits aligned with industrial farming (e.g., uniformity, high yield) over ecological resilience. Traditional seed-saving practices in regions like the Himalayas and the Andes rely on community governance, contrasting with the proprietary control embedded in AI-driven seed patents.

The Green Revolution of the 1960s-70s introduced high-yield wheat varieties that displaced thousands of traditional landraces, leading to a 75% reduction in global wheat biodiversity by the 1990s. The current AI-driven approach mirrors this pattern by centralizing seed selection in the hands of corporations and research institutions, repeating the historical mistake of assuming technological fixes can replace ecological and cultural adaptation. The industrialization of agriculture has consistently prioritized short-term yield over long-term resilience, a trend that AI systems may exacerbate by locking farmers into dependency on proprietary technologies.

In West Africa, farmers use 'farmers' rights' to maintain seed diversity, while in Mexico, the 'milpa' system integrates maize, beans, and squash to enhance resilience without external inputs. The AI model’s focus on monoculture wheat ignores these polycultural systems, which have sustained communities for millennia through biodiversity and ecological balance. Cross-cultural comparisons reveal that resilience is often achieved through decentralized, community-led innovation rather than top-down technological interventions.

AI and drone-based phenotyping can accelerate the identification of drought-resistant traits, but their effectiveness depends on the genetic diversity of the training datasets, which are often biased toward industrial varieties. The scientific literature highlights that genetic erosion reduces the adaptive potential of crops, yet the study does not address how AI-driven selection might further narrow genetic diversity. Additionally, the energy footprint of AI systems (e.g., drone flights, server farms) may offset climate benefits, a factor rarely considered in such studies.

Artistic traditions like the Navajo 'Churro' sheep-breeding practices or the Japanese 'Satoyama' agricultural systems embody a spiritual relationship with land and biodiversity, framing resilience as a sacred duty rather than a technical problem. The AI-driven approach reduces wheat to a data point, stripping it of cultural and spiritual significance, which has historically been a source of adaptive innovation. Indigenous cosmologies often view crops as kin, not commodities, a perspective that could inspire more holistic approaches to resilience.

If AI-driven seed selection becomes dominant, it could lead to a scenario where 90% of global wheat production relies on a handful of patented varieties, creating a monoculture vulnerable to new pests or climate shocks. Future models should incorporate decentralized, farmer-led breeding programs and open-source seed databases to avoid reinforcing corporate control. The scenario also risks exacerbating North-South inequalities, as Global South farmers become dependent on technologies developed and controlled by Northern institutions.

Smallholder farmers, particularly women in the Global South, are the primary custodians of crop biodiversity but are excluded from AI-driven research processes and decision-making. The narrative centers Western scientific institutions and corporate actors, erasing the contributions of indigenous and peasant farmers to wheat resilience. Marginalized voices are also absent in the framing of 'resilience,' which is often defined by industrial metrics (yield, uniformity) rather than ecological or cultural sustainability.