Agriculture & Agri-Business

If you buy feed grain, run a nursery, or grow at commercial scale, AI is worth money in two specific places: timing your commodity purchases better than the market average, and replacing manual counting and monitoring with vision. DataWise built and validated a GPU-accelerated forecasting engine for agricultural commodity prices, wheat, corn, and oils, validated against a real feed mill’s purchasing history and aimed at one decision: when to buy and when to stockpile. Israeli agriculture already proved this discipline scales; we build it at the scale of your operation.

What does AI actually do in agriculture?#

The evidence, with sources labeled honestly:

• Israeli ag-vision went from startup to $300M exit. Prospera, an Israeli computer-vision agtech company, scaled to five times its plan and was acquired by US irrigation giant Valmont for $300 million, per Times of Israel reporting. The technology class, cameras plus models watching crops, is commercially proven at the highest level, and it was proven from here.
• The world’s biggest food buyers use ML price intelligence. Vesper, a commodity-intelligence platform, counts Nestlé and FrieslandCampina among its named customers and publishes its forecast accuracy openly by horizon: 97% at one month out, 85% at twelve. One customer, Futura, credits a single negotiation with a €16,000 saving, per the vendor’s case study. When Nestlé’s buyers use ML price forecasts, the question is not whether the tool class works.
• Buy-timing savings are documented, with caveats. A plastics converter using ChAI’s price forecasting for feedstock buy-timing reports over 10% savings across six months, per the vendor, customer anonymized. We cite it as what it is: a vendor figure, directionally consistent with the rest.
• Data-driven field management is already mainstream on serious farms. In 2023, 70% of large-scale US crop-producing farms used guidance or auto-steer and 68% used yield maps, per USDA data. Vision-based counting and monitoring is the next layer on an already-instrumented base, not a leap from zero.
• Vision at fleet scale is already in the field. John Deere’s See & Spray uses boom-mounted cameras to tell crops from weeds in real time; in 2024 it covered more than 1 million acres and cut herbicide use by an average of 59%, per John Deere. The counting-and-monitoring problem is being solved at scale, not just demonstrated.

This field is younger than retail forecasting and we say so plainly: predictive procurement is emerging, which is exactly why an operator with a validated engine has an edge rather than a commodity tool.

What has DataWise actually built for agriculture?#

• A commodity-price forecasting engine for feed procurement. Built and validated on real data: a GPU-accelerated engine forecasting agricultural commodity prices (wheat, corn, oils) using deep time-series architectures (NBEATS, TFT, TiDE and others) ensembled with gradient-boosted models, SHAP-based explainability so a buyer can see why the model expects a move, and domain feature engineering grounded in feed-formulation chemistry. Validated against a real feed mill’s purchasing history, aimed at optimal buy-timing and stockpiling decisions.
• Advisory work for a plant nursery. Consulted and advised on agricultural planning, procurement optimization, and a roadmap toward vision-based plant counting. That engagement was advisory, and we present it as exactly that: characterization-stage thinking, honestly delivered, including where vision was not yet worth the investment.

Two items, two different verb tiers, both stated at exactly the level that happened. That is house policy.

We buy feed on gut and a Bloomberg screen. What would this change?#

The engine does not replace your buyer’s judgment; it grades and sharpens it. Concretely: a forecast per commodity at your decision horizons, an explainability layer showing which drivers move the forecast, and a track record measured against what actually happened, so within a season you know whether the model deserves a voice in the buy. The decision framework matters more than the model: how many months of cover to hold, when stockpiling beats storage cost, and when to stay out. That framework is what we validated against real purchasing history.

Our operation is not a tech company. What does working with you look like?#

Characterization first: we map your purchasing or growing decisions, test what your data can support, and give you a tested answer before any build. Sometimes that answer is “not yet”, as our nursery advisory work shows, and we consider saying so part of the deliverable. If we do build, you own the result: models, code, and pipelines, integrated into how your operation already works.