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Agriculture

Our Operational AI for agriculture are not only redefining traditional farming practices but also addressing crucial challenges such as food security, resource management, and environmental sustainability.

The use cases of AI in agriculture are both diverse and profound, offering solutions that are as beneficial to smallholder farmers as they are too large-scale agribusinesses. As we look towards a future where sustainability and efficiency are paramount, the role of AI in agriculture is not just promising; it is essential in arriving at a more resilient and bountiful future.

Use Cases

1/8

Precision
Agriculture

AI-driven precision agriculture harnesses data analytics to optimize field-level management. By analyzing data from satellite imagery, drones, and sensors, AI enables farmers to make informed decisions about planting, irrigation, and harvesting, tailoring their approaches to micro variations in soil and environmental conditions.

2/8

Crop Health
Monitoring

Utilizing advanced image recognition and remote sensing technologies, AI systems can detect and diagnose plant diseases and pest infestations early. This capability facilitates timely interventions, reducing crop losses and enhancing yield quality.

3/8

Predictive Analytics
for Yield Prediction

AI algorithms are adept at predicting crop yields by analyzing historical data alongside real-time field data. These predictions aid farmers in planning and optimizing their resources, leading to more efficient farming practices.

4/8

Predictive Equipment Maintenance

The ability to analyze multimodal time-series and vision data from equipment to predict maintenance events, failures, and other anomalies before they occur in order to increase the reliability and performance of farm equipment machinery.

5/8

Automated
Machinery

Self-driving tractors, drones for crop spraying, and automated harvesters powered by AI are revolutionizing field operations. These technologies not only reduce labor costs but also increase precision in tasks such as planting and harvesting, minimizing waste and improving safety.

6/8

Supply Chain
Optimization

AI plays a pivotal role in streamlining agricultural supply chains. From predictive demand analysis to real-time tracking of goods, AI enhances transparency and efficiency in the journey from farm to fork.

7/8

Genetic Crop Improvement

AI-driven genomic analysis is accelerating the development of crop varieties that are more resilient to pests, diseases, and extreme weather conditions. This is a game-changer for ensuring food security in the face of changing global climates.

8/8

Climate & Environmental Monitoring

The ability to analyze multimodal time-series and vision data from equipment to predict maintenance events, failures, and other anomalies before they occur in order to increase the reliability and performance of farm equipment machinery.

Metrics

01

Between 20% to 40% of global crop production is lost to pests annually. Each year, plant diseases cost the global economy around $220 billion, and invasive insects around $70 billion, according to the Food and Agriculture Organization of the United Nations.

Source: USDA

02

Climate change may affect the production of maize (corn) and wheat as early as 2030 under a high greenhouse gas emissions scenario, according to a new NASA study published in the journal, Nature Food. Maize crop yields are projected to decline 24%, while wheat could potentially see growth of about 17%.

Source: NASA

03

For this report, U.S. PIRG Education Fund and National Farmers Union surveyed 53 farmers across 14 states. 
The study found that 53% of the farmers had lost crops because of downtime after a tractor or combine breakdown. Roughly one in three farmers surveyed fear losing their family farm to such a breakdown.

Source: PIRG.ORG
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