SACRAMENTO, CALIF.  — Satellite imagery, artificial intelligence and advanced modeling are poised to change how crop conditions and agricultural production are monitored and measured.

A Sacramento-based startup believes that harnessing these advances will diminish reliance traditional crop analysis tools, including surveys, historical analogs, weather correlations and vegetation indexes.  Building on these traditional tools used for generating projections, SatYield uses a proprietary biophysical simulation system and real-time observation.

At the center of this shift is remote sensing, the use of satellite observations to monitor crop conditions across large geographic areas. Publicly available satellite networks continuously collect imagery that may be analyzed using computer vision and machine learning tools, allowing companies to track crop development throughout the growing season.

The technology reflects a broader transition from periodic crop assessments toward continuous observation. Instead of surveys or monthly snapshots, new systems incorporate imagery, weather data and crop measurements as they become available, creating a constantly updated view of production potential.

“We analyze the pixels, and we create an intelligent layer,” said Gabby Nizri, co-founder and chief executive officer of SatYield. “What you and I can see from space is just pixels or nice images. We are translating them to insights.”

The technology goes beyond identifying what is planted in a field. Modern analytical systems combine satellite imagery with weather data, soil information and agronomic models to estimate acreage, monitor crop health and assess production potential. Increasingly, these platforms are designed not simply to track weather conditions but to measure how plants respond to those conditions.

At the heart of the company’s approach to assessing crops is the concept of an agricultural digital twin, a virtual representation of the crop based on inputs that include satellite images, weather, soil conditions and planting information. The digital twinning allows researchers and analysts to simulate how plants develop under varying conditions.

“We grow crops inside a computer,” Nizri said.

Digital twin models integrate information ranging from planting dates and soil characteristics to weather forecasts and satellite observations. The result is a dynamic simulation that can generate thousands of potential crop outcomes, helping analysts estimate yield and production as a season unfolds.

“Rather than only asking what the crop looks like today, SatYield is designed to help answer a more forward-looking question: Given what is observed today, where is production heading?” the company said.

David Shoaf, an agriculture industry veteran and strategic sales consultant with SatYield, said the company’s system combines satellite imagery with AI-based crop classification and crop-growth simulations.

 “We take those inputs, the soils, the weather, the practices, the planting dates, the seed, and we put it into our algorithm,” he said. “Then we create about 20,000 different crop simulations.”

For analysis, biological measurements are growing in importance, lessening dependence on visual indicators. One example is leaf area index, a metric that measures the amount of sunlight absorbed by crop canopies. Because plant growth is closely tied to energy capture, these measurements can provide insight into crop vigor, stress levels and yield potential.

While providing updates more frequently than the US Department of Agriculture, SatYield does not view its services as a replacement for the government information. Instead, the company seeks a leadership position as the provider of an “independent, higher-frequency view that can confirm, challenge or add regional detail between official releases.”

In addition to offering aggregate crop forecasts, SatYield’s technology also offers more granular insights into regional production trends.

 “For grain merchandisers, traders, food companies and other agricultural market participants, the objective is to identify changes in physical supply earlier, understand where those changes are occurring and continuously reassess production risk as the season develops,” Nizri said.

SatYield is seeking to complement established market information with a higher-frequency view of the crop itself, based on weather satellite observations and the biophysical simulations showing how the plant is responding.

“By updating that view throughout the season, SatYield aims to surface meaningful shifts in acreage, yield and production before they become obvious in traditional market data,” Nizri said.