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How Is External Data Used in Agriculture?

Every aspect of agriculture requires a wide range of specialized data and decision-making abilities. However, people who work in traditional farming and ranching cannot take in real-time data about every aspect of a field or animal as computers can. Additionally, the ability to predict anything from crop yields to breeding program success is more difficult for humans than for machines.

Subsequently, AI-based agriculture is used because it is able to take in real-time data on anything. For example, farming solutions manage data on micro-climate, soil quality, plant classification, weather, historical yield, employee data, and more. Similarly, livestock management solutions use data on animal classification, animal behavior and appetite, feed and water quality, and so on.

How are Machine Learning Models Used in Agriculture?

Machine learning optimizes the proper management of crops and livestock through the constant, real-time collection of data on the minutest level. This happens through the use of satellite data, sensors, cameras, and robots. They can identify predatory insects and diseases with image recognition, predict plant yields or the most likely successful new plant strains with predictive algorithms, and use technology to constantly monitor animal health and waste.

Which Companies Lead the Way in Building Advanced Analytics and Machine Learning Products in This Field?

AbuErdan uses machine learning models to manage chicken production, from breeding and hatchery to broiler and slaughter. Sensors and cameras record animal behavior, feed and water data 24/7, while artificial intelligence optimizes chicken health and production.
Founded by scientists from rural areas, PEAT has a global vision to reduce food insecurity and improve the environment. It developed Plantix, a mobile app available in 18 languages that uses machine imaging to identify 30 major crops and diagnose pest predation, diseases, and nutrient deficiencies.
Trace Genomics
Combining genomics, biology, and machine learning, Trace Genomics collects soil samples from farmers and reports on the chemistry and microbiological content. Farmers can then make informed decisions about crops, diseases, fertilizers, and more.

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