Using AI to Predict Crop Yields

At Xyonix, we regularly build AI and machine learning models to make predictions based on structured and unstructured data like crop yields.

Combine harvester harvesting wheat

Why Predict Crop Yields?

Predicting crop yields is critically important to the global food production ecosystem. Farmers can make better decisions with access to quality crop yield predictions, government policy makers often use accurate crop yield predictions to strengthen national food security, and companies that produce seeds often predict how well new plant variations grow in different environments.

AI models developed from diverse data sources:

Aerial Imagery Analysis

Similar to how marine biologists assess coral reefs, we use AI to analyze aerial imagery to monitor crop health and growth.

Tailored AI Models

We build custom models to assess crop density and environmental impact, providing valuable insights.

Time Series Data Forecasting

We use AI to predict future crop yields by identifying complex patterns in historical data.

Enhanced Agricultural Decision Making

These predictions help optimize planting, irrigation, and harvesting schedules.

Agriculture Price Forecasts

We use AI to analyze historical data and market trends to forecast future crop prices.

Optimize Your Agricultural Output with AI

We have extensive experience building predictive models on a variety of different types of data. Our custom models will give you the ability to accurately and efficiently predict crop yields to optimize your agricultural outputs.

Check out our projects to get a taste of our capabilities:

projects
Manufacturing Engagement Optimizer

Manufacturing Engagement Optimizer

We worked closely with a large industrial parts marketplace provider to help them better understand their current customers and partners. We developed AI models trained on their customer and partner site usage, profile and external attribute data to analyze engagement. We were able to successfully forecast which customers and partners were destined for high and low engagement. Perhaps more importantly, our models were able to explain why customers were likely to be high or low engagers. These deep, data driven insights, when used to power a living intelligence dashboard, are able to direct sales and marketing activities so they are more efficient and impactful.

Customer Behavior Analyzer

Customer Behavior Analyzer

We worked very closely with executives of a major dental insurance company to help better understand their customers. We used historic data to predict future customer behavior and purchase selections. Our models predict which customers are likely to lapse, and which are likely to remain loyal. Our models are also explainable, so it is easier to understand which attributes of a customer are indicative of loyalty or lapse tendencies. In addition to understanding high level customer characterizations, our explainable models describe why a specific customer is predicted to be a lapser or loyalist.

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Henry Albrecht

“Xyonix is not some local ML leader — they’re global gurus with the technical and strategic skills to transform your business. Xyonix helped me think differently.”

Henry Albrecht, CEO @ Limeade, Inc. [more]

Want to dig deeper? Read our article about how AI is transforming agriculture below.

Featured
Farming is Dying – Can AI and AgTech Save It?
Farming is Dying – Can AI and AgTech Save It?

The global food supply is on the brink of crisis. Facing challenges like an aging workforce, labor shortages, declining crop yields, and climate change amongst others, farming is in dire need of a lifeline. AI and AgTech are emerging as the saviors of agriculture, transforming everything from crop breeding to farm-to-table traceability. These revolutionary technologies promise to enhance sustainability, improve yields, and reduce environmental impact. With the urgency for innovation greater than ever, AI is the essential lifeline the agricultural industry needs to secure a resilient and efficient future.

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Drones to Robot Farm Hands, AI Transforms Agriculture

From detecting pests to predicting which crops will deliver the best returns, artificial intelligence can help humanity oppose one of its biggest challenges: feeding an additional 2 billion people by 2050 without harming the planet. AI is steadily emerging as an essential part of the agricultural industry’s technological evolution including self-driving machinery and flying robots that are able to automatically survey and treat crops.

References

[1] Horie, T., Yajima, M., and Nakagawa, H. (1992). Yield forecasting. Agric. Syst. 40, 211–236. doi: 10.1016/0308-521X(92)90022-G

[2] Syngenta (2018). Syngenta Crop Challenge In Analytics. Details at: https://www.ideaconnection.com/syngenta-crop-challenge/challenge.php/