🇺🇸 Anisha Dhoot

The two halves of the project answer different questions. KelpNutriBoost addresses input cost and carbon: kelp is abundant along the Oregon coast, grows without fertiliser or fresh water, and carries the nitrogen and trace minerals that make seaweed extracts a long-established soil amendment. Blending it with biochar — a stable, charcoal-like carbon produced by heating organic material without oxygen — is a technique with a substantial research literature behind it, valued for retaining nutrients and holding water in the soil.

The unusual element is what she made the biochar from. Rather than agricultural residue, Dhoot used recycled plastic and treated sewage as feedstock, which turns two waste streams that ordinarily cost money to dispose of into a soil input. That is the kind of substitution that only looks obvious afterwards, and it is the part of the project that moves it from a school science exercise toward something with an actual economic argument.

Earth-AI is the second component and, in some ways, the more demanding one for a 16-year-old: a machine-learning model that takes local climate and soil conditions and predicts what will grow. Dhoot taught herself to code through online courses in order to build it. Together the fertiliser and the model form a platform she calls Smart Carbon Recycling — inputs on one side, a decision tool on the other.

The Gloria Barron Prize for Young Heroes selects fifteen winners each year from hundreds of nominations across the United States and Canada, and awards each of them $10,000 to continue their work. It is a service and leadership award rather than a pure science competition, which is worth stating precisely: the prize recognises the project and its application, not a peer-reviewed result.

That distinction is the honest frame for this entry. What is documented is a self-built platform with two working components, a national award decided by an outside panel from a large nominee field, and coverage in Oregon news outlets and on public radio. What is not documented is field-scale yield data. The claim on this page is the one the record supports: a 16-year-old designed, built and won national recognition for an agricultural system combining a waste-derived fertiliser with a self-coded predictive model.

Nitrogen fertiliser is responsible for a substantial share of global agricultural emissions, both in its manufacture — the Haber-Bosch process is energy-intensive and largely fossil-fuelled — and in its use, where excess nitrogen runs off into waterways and is released as nitrous oxide. Seaweed extracts and biochar are two of the most-studied lower-carbon interventions in that problem, which places Dhoot's project inside an active research field rather than beside it.

Biochar's appeal is that it addresses two things at once: it sequesters carbon in a stable form that resists decomposition for centuries, and it improves soil structure by retaining water and nutrients that would otherwise leach away. Producing it from recycled plastic and treated sewage rather than crop residue is the less conventional choice, and it is the one that gives the project a waste-management argument alongside the agricultural one.

The machine-learning half deserves its own note. Crop-suitability modelling from climate and soil variables is a standard problem in agricultural data science, and building a working version of it requires assembling data, choosing features, training a model and evaluating it — a sequence that is taught at university level. Doing it from self-directed online coursework at 16 is the part of the project that is hardest to shortcut, and it is why the entry describes a platform rather than a single product.

The honest limitation of any high-school science project is scale, and it applies here. A biofertiliser validated in trials is not a biofertiliser validated across a growing season on working farmland, and a crop-suitability model trained on available data is not one tested against yields it did not see. Dhoot has not claimed otherwise, and neither does this page. What is recorded is a completed, functioning platform of two parts, built by a sixteen-year-old who taught herself the second half, and recognised by a national award decided from hundreds of nominations across two countries.