- Data Categories
Agriculture Data 
B2B Data 
Biometric Data 
Company Data 
Conservation Data 
Demographic Data 
Economic Data 
Environmental Data 
Geospatial Data 
Healthcare Data 
Individual Data 
Industry-Specific Data 
Internet of Things (IoT) Data 
Legal and IP Data 
People and Employment Data 
Political Data 
Psychology Data 
Real Estate Data 
Retail & Commerce Data 
Social Data 
Space Exploration Data 
Sports & Entertainment Data 
Web Data
- Data Categories
- Agriculture Data
- B2B Data
- Biometric Data
- Company Data
- Conservation Data
- Demographic Data
- Economic Data
- Environmental Data
- Geospatial Data
- Healthcare Data
- Individual Data
- Industry-Specific Data
- Internet of Things (IoT) Data
- Legal and IP Data
- People and Employment Data
- Political Data
- Psychology Data
- Real Estate Data
- Retail & Commerce Data
- Social Data
- Space Exploration Data
- Sports & Entertainment Data
- Web Data
Agriculture
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
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.
PEAT
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.
Connected Datasets
PipeCandy eCommerce Leads & Insights for Fulfillment
PipeCandy eCommerce Leads & Insights for Fulfillment tracks company data and company shipping details: shipping volume, which companies they use to ship products, whether they ship internationally or not, and so on. You can find company leads easily with this dataset with filter capabilities.
Wikiroutes Transit Data
Wikiroutes Transit Data provides public transport information—routes, stop points, and more—via crowd-sourcing. The data is constantly updated and can be easily converted and integrated into your own software system.
Wikiroute’s Transit Data is used by individuals, private companies, and government agencies of all types and sizes.
TrackStar Predictive Credit Technology
TrackStar’s Predictive Credit Technology uses fifteen years of financial dispute data to create predictive models of future borrowing potential. With this data and AI technology, your bank or other lending company can mitigate the risk of fraud, improve existing customer relations, and reduce your operating costs.