Yield estimation using satellite imagery, machine-learning & advanced biophysical stress index

Crop yield prediction is a cornerstone of modern agricultural planning, enabling entities across the supply chain to anticipate production outcomes, allocate resources efficiently, and respond to climate variability. Technologies such as NDVI-based remote sensing, IoT sensors, and weather-driven crop models have advanced yield prediction solutions, however common limitations include an overreliance on vegetation indices, requirement for extensive ground-truthing, lack of capturing environmental stressors such as drought or heat and difficulty with transferability across regions. This limits predictive accuracy, especially under variable conditions. The CropVision technology addresses these gaps by integrating remote sensing with a simulated biophysical stress index, offering a scalable, data-efficient solution for field-level yield prediction.