Monitoring Crops with AI

At Xyonix we have deep expertise analyzing multispectral imagery and building machine learning and AI models to automatically identify crops and assess crop health.

AI Drone scanning agriculture land monitoring crops

Low cost and accurate assessment of crop and soil health has long been key to a successful farm and agricultural economy. Recent advancements in drone and satellite technology enable the acquisition of cost effective, timely and localized high resolution multispectral imagery of agricultural land. AI and machine learning offers the ability to recognize highly valuable patterns in this and similar imagery.

Governments often want to understand where soil is degrading and inventory which crops are present (crop identification) in which areas. NGOs and hedge funds often want to predict future yields [1], the former to predict food scarcity, the latter to modify purchases in wheat, corn and other futures. Farmers want to know exactly which crops to spray with fertilizer or pesticide, thus reducing cost and impact to the environment.

Does your application gather agriculture related imagery? If so, contact us, and perhaps we can help you glean the insights that will transform your business.

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“Xyonix dug-in and produced for us that rarest of discovery reports: highly technical findings, expressed with simplicity and clarity, so that everyone on the team, regardless of role, could benefit from this additional understanding.”

— David Nett, Senior Director of Emerging Technologies @ RLLeaders [more]

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

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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.

References

1. Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data, Jiaxuan You et al.
2. Machine Learning in Agriculture: A Review, Liakos et al