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From Guesswork to Growth: How AI & Geospatial Data Increased Crop Yield for an AgTech Startup

Feeding a growing world demands smarter farming. This case study explores how BayRock Labs and an AgTech startup joined forces to revolutionize soil management with data science and AI, boosting yields and protecting the environment.

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From Guesswork to Growth: How AI & Geospatial Data Increased Crop Yield for an AgTech Startup
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Value We Added

Increase crop yield

Increase crop yield by 20% through precise nutrient application based on real-time data insights.

Reduce environmental impact

Reduce environmental impact by 35% with optimized fertilizer use, minimizing nutrient runoff and promoting sustainable agricultural practices.

Make data-driven decisions

The platform empowered informed decision-making for soil health management, contributing to a more abundant and sustainable global food supply.

Challenges

Inefficient Soil Testing

Traditional methods lacked precision, leading to suboptimal fertilizer use and reduced crop yield.

Environmental Impact

Inefficient fertilizer application contributed to up to 50% nutrient runoff, harming water quality and ecosystems.

Data Gaps

The absence of actionable insights from soil data made it challenging to optimize soil fertility management for farmers and agronomists.
From Guesswork to Growth: How AI & Geospatial Data Increased Crop Yield for an AgTech Startup
Interested in reading the entire case study, Download the complete case study now.
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Approach

We collaborated with the client to develop a data-driven platform using advanced data science and cloud engineering:
AI & Machine Learning

Integrating algorithms to analyze real-time and historical data (soil composition, weather) for customized fertility plans.

Scalable Cloud Infrastructure

Leveraging Google Cloud Platform (GCP) for efficient storage and processing of large agricultural datasets.

Geospatial Data Integration

Employing GIS and Remote Sensing techniques (e.g., satellite imagery) to incorporate spatial information for a comprehensive view of soil health across vast areas.

Outcome

Enhanced Crop Yield

The implementation of our AI-driven platform led to a 20% increase in crop yield by enabling precise nutrient application based on real-time soil data.

Reduced Environmental Impact

Optimized fertilizer use reduced nutrient runoff by 35%, contributing to more sustainable agricultural practices.

Empowered Decision-Making

The platform provided farmers and agronomists with actionable insights, enabling data-driven decisions that promote long-term soil health and agricultural sustainability.

Conclusion

Our data-driven approach revolutionized soil management, paving the way for a future of precision agriculture that optimizes yield, minimizes environmental impact, and fosters long-term agricultural sustainability.

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