Innovated in Weather Forecasting and Operational Planning​

Aquila collaborated with a Global Food Conglomerate to pioneer a groundbreaking weather analysis solution aimed at optimizing agribusiness operations. The goal was to provide comprehensive and accurate weather insights for the management of crops and cattle in a vast geographic footprint that spans 9 US states, Canada (3 provinces) , Mexico (2 States), Colombia, Brazil  5 States), Argentina (1 province), Angola (3 Farms), and Spain (1 Farm).

The Challenges:

The agricultural industry relies heavily on precise weather data to make informed decisions regarding planting, harvesting, and managing livestock. Accurate weather forecasting and operational efficiency are paramount for ensuring the success and sustainability of agribusiness.The

The Solution:

Aquila introduced an advanced, deep AI model that revolutionized weather forecasting for agribusiness. Their solution incorporated a wide range of data sources and cutting-edge technologies, including:

1. Vertical Atmosphere Profiles:

• Pressure

• Temperature

• Density

• Refractivity

2. NOA Data Layers:

• - Radar data

• - Weather forecasts and models

• - Historical climate records

• - Climate models and projections

• - Carbon dioxide and greenhouse gas data

• - Air quality data

• - Water quality data

• - Land cover and land use data

• - Coastal Models

• - Real-time and historical disaster data

• - Hazard maps and warnings

• - Post-event analysis and assessment data

• - Earthquake and tsunami data

• - Volcano monitoring data

• - Space weather information

3. Additional Layers:

• - Satellite Radar and Imagery

• - Land use change detection

• - 2D and 3D Extraction

Outcomes:

Aquila's deep AI model delivered a range of critical outcomes for the Global Food Conglomerate:

1. Improved Efficiency and Planning: 

• Significant improvements in the time required to analyze and recognize land use for agribusiness applications. 

• Enhanced ability to forecast future weather conditions for optimal planning and resource allocation.

2. Standardization and Collaboration: 

• Unification of weather analysis standards and scoring systems across the conglomerate's multiple departments and countries. 

• Facilitation of cross-border collaboration and knowledge sharing, promoting a uniform approach to weather analysis and planning.

3. Agility in Information Integration:

• The system enabled the seamless integration, processing, and sharing of local weather information obtained from various national and regional agencies within the all 56 farms involved in the project.

• Local agencies and departments now have a streamlined mechanism for providing and accessing critical weather data.

Conclusion:

Aquila's innovative deep AI model, with its extensive data sources and advanced technologies, has revolutionized weather analysis for agribusiness. The Global Food Conglomerate now benefits from more accurate and timely weather insights, allowing for better planning and decision-making across a vast and diverse geographical landscape. This groundbreaking project serves as a model for the potential of AI and technology in agriculture, setting new standards for operational efficiency in the agribusiness sector.