The research, conducted by Kavali Munisekhar and Bangalore Morarji at Vellore Institute of Technology, develops the Human-Centred Constitutional Urban AI Governance Framework and operationalises it as the Constitutional Smart-City Governance Index, or CSGI. The instrument scores urban AI against Article 21 of the Indian Constitution, human-centred AI principles, and Sustainable Development Goals 11 and 16. Six pillars—constitutional rights, human-centred AI, sustainable development, service delivery, participation, and accountability—comprise thirty-eight indicators with operational definitions, official secondary-data sources, a transparent normalisation-and-weighting model, and a validation strategy. The framework is deliberately positioned at the intersection of urban studies, public administration, law, and AI governance: the Constitution supplies the normative foundation, but the analytical machinery is that of governance research rather than doctrinal legal commentary.

What makes Article 21 an unusually rich constitutional resource for exactly this task is five decades of Supreme Court jurisprudence that have expanded it from a procedural shield into a substantive charter of dignified urban life. Shelter and livelihood were recognised in Olga Tellis v. Bombay Municipal Corporation in 1985, dignity and the bare necessities of life in Francis Coralie Mullin in 1981, a clean environment in Subhash Kumar v. State of Bihar in 1991, and, decisively for the digital era, informational privacy subject to a structured proportionality test in Justice K.S. Puttaswamy v. Union of India in 2017. When an integrated command and control centre fuses surveillance feeds, when a welfare platform authenticates biometric identity before releasing entitlements, or when a predictive model allocates sanitation resources across neighbourhoods, Article 21 is not a peripheral legal technicality; it is the constitutive standard of legitimate state action.

The CSGI was applied to four leading Indian smart cities—Bengaluru, Pune, Surat, and Indore—selected for their AI maturity, domain diversity, and prominence within national urban policy. Bengaluru’s portfolio is dominated by mobility and public safety, including adaptive sensor-driven traffic signal control and an extensive Safe City surveillance programme integrating video analytics with policing command centres. Pune centres on an integrated command centre coordinating traffic, emergency, and utility operations alongside platform-based citizen engagement channels. Surat emphasises hydrological monitoring and flood early warning linked to upstream dam management, while Indore exemplifies the operational-excellence pathway built on comprehensively re-engineered waste management with GPS-tracked collection fleets and monitored processing facilities.

The governance-opacity audit that followed produced the study’s most striking finding. The four cities are 86% to 93% opaque on the transparency and accountability artifacts a rights-based assessment requires. No city publishes a data-protection impact assessment for its AI or surveillance systems, none maintains a public register of deployed algorithms, none has adopted a municipal AI-governance policy, and none subjects its algorithmic systems to independent audit—each of these four artifacts is absent in 100% of the cases examined. Only integrated command centre operational reporting and special purpose vehicle-to-council reporting exist in even rudimentary form, and only partially. Bengaluru, the city that tops the national liveability ranking, is on this measure the most opaque of the four. The finding reframes the study’s data scarcity: the missing values on the rights and accountability pillars are not a gap in the researcher’s dataset but a measurement of a structural condition—Indian urban AI is being deployed faster than the disclosure and audit infrastructure that would make it accountable under Article 21.

The index-divergence analysis then exposed what the researchers call a constitutional blind spot in India’s urban metrics. The CSGI correlates strongly negatively with the Ease of Living Index (Spearman ρ = −0.80) yet positively with the Municipal Performance Index (ρ = +0.80) and the Swachh Survekshan cleanliness survey (ρ = +0.95). Bengaluru crystallises the divergence: first on liveability, it ranks last on municipal performance, last on cleanliness, and last on the CSGI. The Ease of Living Index, in turn, is negatively concordant with every other instrument. When such a metric conditions reputational and increasingly fiscal rewards, it steers cities toward the dimensions it can see and away from the rights and accountability dimensions it cannot. An incentive that is blind to rights and accountability will predictably produce cities that are optimised for visible liveability and hollow on constitutional substance.

A Monte Carlo weight-robustness test addressed the objection that composite index rankings are artefacts of the analyst’s weights. The CSGI was recomputed for 20,000 pillar-weight vectors drawn uniformly from the weight simplex, so that every admissible weighting—including schemes that all but ignore the constitutional pillars—is equally represented. Bengaluru ranks last under 100% of the sampled weightings: there exists no admissible combination of pillar weights on which it escapes the bottom position. Its entire CSGI distribution (90% interval 1.55–2.50) lies below those of the other three cities. The ordering among the top three is genuinely weight-dependent—Surat leads under 45% of weightings, Pune under 40%, and Indore under 15%, with none ever ranking last. The substantive claim the paper rests on is therefore the robust one: whatever normative weight a reader assigns to constitutional rights relative to efficiency, the city that leads the national liveability ranking occupies the lowest position on a rights-inclusive assessment across the entire weight space.

The pillar-by-pillar evidence reveals an inverted governance profile: every city scores highest on the outcome pillars—sustainable development and service delivery—and lowest on the normative and institutional pillars of constitutional rights protection and institutional accountability. Indian urban AI, on this evidence, is operationally capable and constitutionally under-governed. Surat’s flood early-warning and utility analytics constitute a substantive resilience achievement aligned with SDG 11.5 and protective of life in the literal Article 21 sense, while Indore’s waste system delivers measurable SDG 11.6 outcomes. Yet the distributional caveat is systematic: area-based development concentrated smart infrastructure in selected zones, and data coverage of informal settlements lags, so gains are unevenly experienced. Human oversight is strongest where operational stakes enforce it—Surat’s flood early-warning embeds forecasts in an emergency-management chain in which officials interpret model outputs, exemplifying the high-automation/high-control combination that human-centred AI prescribes—but transparency, explainability, and independent oversight are largely absent.

The paper’s central interpretive claim is that constitutional safeguards should be understood as infrastructure for sustainable urban AI, not as external constraint upon it. Doctrinally, Article 21 already contains the evaluative materials—dignity, livelihood, environment, privacy, fair procedure—that AI ethics frameworks attempt to supply from softer sources; anchoring urban AI governance in Article 21 converts persuasive principles into enforceable standards with an existing remedial apparatus. Institutionally, constitutional anchoring supplies what the global AI-principles consensus lacks: a domestic adjudicator, a body of interpretive method in the proportionality test, and legitimacy independent of technology vendors and executive discretion. Instrumentally, the sustainability evidence suggests constitutional compliance and SDG performance are complements, not substitutes: Surat’s life-protective early warning and Indore’s environmental gains are advances in Article 21’s substantive guarantees, and the trust generated by rights-respecting design is itself a resource for the sustained citizen cooperation on which sanitation, resilience, and mobility systems depend.

The study offers tiered recommendations sequenced from disclosure through assessment to independent oversight, designed to be adoptable within existing legal authority. The Government of India should condition central assistance for AI-enabled urban projects on a published Constitutional and Data-Protection Impact Assessment applying the Puttaswamy proportionality test, and task the IndiaAI Mission’s governance apparatus with a national public register of urban algorithmic systems. The Ministry of Housing and Urban Affairs should extend the DataSmart Cities Strategy into a Model Municipal AI Governance Policy with procurement clauses for explainability, bias testing, audit rights, and exit provisions, and rebalance mission-evaluation instruments so rankings reward published impact assessments, deliberative participation, and functioning algorithmic grievance routes alongside output metrics. State governments should notify municipal AI oversight provisions under state municipal acts, including council approval for surveillance-capable deployments. Urban local bodies should institutionalise upstream participation through ward-committee deliberation on proposed deployments before procurement, and extend grievance platforms with an algorithmic-decision category carrying defined timelines and a human-review guarantee. Smart City special purpose vehicles should report AI deployments, incidents, and audit findings to the council on a fixed calendar and publish system inventories in open formats. The Data Protection Board should prioritise urban public-sector deployments and publish reasoned decisions, while the Comptroller and Auditor General should pilot performance-audit methodology for urban algorithmic systems. The researchers acknowledge that the present application is an illustrative pilot rather than a validated measurement—because comparable cross-city data remain sparse, the data-scarcity itself is a substantive finding and a precondition the field must remedy before a fully quantitative CSGI can be validated. But the diagnosis is unambiguous: the right to life is being advanced in outcome and exposed in process, and the exposure is systemic rather than city-specific. Cities that govern AI through their constitutional commitments will not merely avoid harm; they will convert the Constitution’s oldest promise, the protection of life, into the organising purpose of their newest infrastructure.

Subject of Research: AI-enabled smart cities and the constitutional right to life in urban India

Article Title: Can AI-enabled smart cities advance the constitutional right to life? A rights-based index, governance-opacity audit, and robustness analysis for sustainable urban India

Article References: Can AI-enabled smart cities advance the constitutional right to life? A rights-based index, governance-opacity audit, and robustness analysis for sustainable urban India. (n.d.). https://doi.org/10.3389/frsc.2026.1959012

Image Credits: AI Generated

DOI: 10.3389/frsc.2026.1959012

Keywords: artificial intelligence, smart cities, constitutional governance, human-centred AI, urban India, SDG 11, digital governance, AI-enabled, smart, cities, advance, constitutional

The adoption figures reveal how far temperate agroforestry still has to go. An analysis of the 2015 European Land Use and Land Cover Survey found that agroforestry was practised on only 1.6 percent of Germany’s agricultural land, despite decades of scientific enthusiasm. The barriers are consistently technical and agronomic: a lack of suitable machinery, limited mechanization options, insufficient knowledge and the absence of established best practices. Management complexity and missing technologies significantly hinder adoption, and mechanization and labour challenges recur across European studies. High initial investments, bureaucratic hurdles and labour-intensive pest and disease control compound the perceived risk, particularly in regions dominated by monocultures and short-term production thinking. Although the European Union and its Member States have expanded funding for agroforestry, and German law has offered greater legal certainty for such systems since 2023 through the regulation governing direct payments, the day-to-day difficulty of running a mixed tree-crop system remains a formidable obstacle.

Within an alley cropping system, the understory vegetation strip is a semi-natural habitat typically dominated by perennial species. It can be sown deliberately during establishment, using flowering or grass mixtures, or it can develop spontaneously from the seed bank and surrounding vegetation. Its management matters enormously because the strip engages in diverse competitive and complementary interactions above and below ground with every other component of the system: the trees, the field margin that extends a few metres into the crop area, the field core beyond it, and the fauna that moves among them. Vegetation in the strip is usually controlled by cutting, mowing or herbicides, with frequencies ranging from several times a year to limit competition with young trees, to once every few years in older systems. Farmers typically pay more attention to the vegetation immediately around trees than to the plants between them within a row.

Ecological succession is one of the central drivers of management demand. When an alley cropping system is established on a former monocropped arable field, post-agricultural vegetation development begins within the strip, following the dynamics of natural ecosystems. If succession proceeds without active management, spontaneous woody vegetation can establish itself, which then reduces the efficiency of subsequent operations such as mowing. Biodiversity adds a second layer of complexity. Agroforestry and its vegetation strips increase the abundance and diversity of several taxonomic groups, largely because greater habitat complexity creates a wider range of food resources, living spaces and microclimatic conditions. But without management, strips can degrade into uniform grass areas with declining flowering species richness and lower abundance of wild bees, honeybees and hoverflies. Plant diversity itself can cut both ways: some pests and pathogens have broad host ranges, so a richer plant community can maintain them as reservoirs and make their control harder.

Farmers also worry, with some justification, that their perennial grass strips may be a source of weed infestations that disperse into the crop area and depress yields. Weeds from the strips can indeed colonise the field margin and field core through plant spillover via different dispersal strategies. However, research shows that this spillover is largely restricted by dominant and competitive perennial and annual species to the field margin, the first ten metres from the grass strips, where the effect on weed assemblages is significant. Compared with conventional arable systems, alley cropping tends to suppress annual disturbance-tolerant weeds while favouring perennial species with creeping growth habits and late, short flowering periods, which can become problematic in no-tillage or reduced-tillage systems.

The strips are simultaneously habitat for pests and for the natural enemies of pests, which makes their ecological role genuinely ambivalent. Increasing habitat complexity can raise herbivore abundance and plant damage in alley cropping. The structural elements of these systems form optimal habitats with rich dietary composition for rodent pests such as the common vole, whose feeding on plants and tree roots, network of runs and girdling of trees can cause damage on a large scale. At the same time, the strips promote predatory birds and predatory arthropods including spiders and carabid beetles, and predation rates in the strips exceed those of simplified arable systems. The lifespan and fecundity of many beneficial insects, especially pollinators, increase within nectar- and pollen-rich plant communities, and the strips provide overwintering habitats that raise spider diversity and pest control potential.

Below ground, the competition is just as intricate. Trees, strip plants and crops engage in complex inter- and intraspecific struggles for water and nutrients. In sandy soils and regions with low annual precipitation, such as parts of north-eastern Germany, cereal, potato and vegetable production is limited by competition for water. Zones of root competition between strip plants, trees and crops can extend several metres into the field margin, where root competition between trees and crops reduces yields, relative growth and soil moisture while increasing tree root dominance and producing high variation in crop root properties. Rooting systems vary along horizontal and vertical gradients depending on tree species, soil properties, sand content, soil layer, stand age, tillage frequency and distance from the tree row, with higher root densities in light soils.

The physical environment of the strips poses challenges that set them apart from ordinary flower strips or field edges. They are littered with stones, branch cuttings from pruning, molehills, dead vegetation that can exceed one metre in height, standing and fallen wooden poles for fastenings, holes created by field mice, plough furrows and tree guards such as plastic or organic mulching. Strip width compounds the difficulty: in narrow strips of only one to two metres, equipment must be compact enough to pass between the trees, while in ploughed systems a furrow at the boundary between strip and alley can create height differences of up to thirty centimetres. As biomass accumulates during the growing season, the vegetation overgrows these objects, further complicating any mechanical operation.

This is where the authors see the opening for smart management. They define smart field robotics as systems capable of autonomously navigating within and between the strips and performing physical management and monitoring tasks. Sensor systems are needed for environment perception, to detect trees and obstacles, and for localisation and positioning, so the robot can follow a specific path or achieve proper coverage of the strips. Processing sensor data with computer vision or object detection models allows detected trees to serve purposes beyond navigation: identifying areas around trees or zones with specific weed pressure would enable more intensive management of those areas within a precision agriculture framework. The same repeated traversal could support tree monitoring, early disease detection, determination of optimal harvest time for fruit trees, and optimal placement of pruning cuts for knot-free high-value timber, all well suited to decision support systems that optimise practices and flag problems early. Equipped with sensors capable of detecting target organisms, the robots could also enable terrestrial biodiversity monitoring, tracking species abundance and community composition over time.

Significant hurdles remain before such robots become routine. Alley cropping sites vary widely in tree species and varieties, age, spacing and management history, and conditions shift across space and time within a single system, so universal software and hardware solutions are unlikely to work without system-specific calibration and tailored training datasets. Reliable operation, such as hoeing around tree bases, demands high spatial precision to avoid mechanical damage to young trees, raising technical complexity and costs. Economic feasibility is a further question, since the strips typically occupy only two to forty percent of the arable field, so dedicated machines must prove their worth within whole-farm operations. Automated mowing has already scaled in predictable environments such as golf courses, sports fields and airfields, and automation of non-routine agricultural tasks can be achieved at economically viable costs, but alley cropping will demand more adaptable machines. If those machines materialise, the study concludes, smart vegetation management could optimise mowing regimes, improve tree health and pest monitoring, reduce labour requirements and strike the long-sought balance between biodiversity conservation and agricultural productivity, potentially transforming the feasibility and broader adoption of agroforestry across temperate landscapes.

Subject of Research: Agriculture

Article Title: Towards smart management of understory vegetation strips in temperate alley cropping systems

Article References: Donat, M., Bajalan, I., & Bloch, R. (2026). Towards smart management of understory vegetation strips in temperate alley cropping systems. One Ecosystem, 11, Article e186665. https://doi.org/10.3897/oneeco.11.e186665

Image Credits: AI Generated

DOI: 10.3897/oneeco.11.e186665

Keywords: agroforestry, alley cropping, understory vegetation strips, field robotics, biodiversity, weed management, precision agriculture, temperate agriculture, Towards, smart, management, understory