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From image to data

right from the farm

AgrX works with Computer Vision & Machine learning to find new solutions to old problems



Real-time solutions for agricultural challenges using artificial intelligence and computer vision

End-to-end Computer Vision solutions for agriculture

Raw data collection

We adapt the best hardware available and find cost-effective field solutions to capture data in every situation, using our experience in rough environments like farms.



Using Deep Learning, Machine Learning and Computer Vision  tools and frameworks, we find pathways to extract the key features that the client needs, typically in agricultural setups.

Automated Analytics

We provide analytics right from the field, automating outputs, predictions, algorithm and re-training, as well as we gather data from other sources to find patterns or specific data points.



We provide reports, web feeds, alarms, heatmaps or other tools that are convenient for the client, in different possible formats, always suitable for the farming environment.


We have been selected by:

Our portfolio of work

Our areas of expertise




A system to monitor pollination events, thus, efficiency, in different crops using imagery, automatically.

FRUIT recognition 

COUNTING & sizing in

real time

A system to count fruit in real time on the very harvester, as a way to have a heatmap on yield for every lot.

FLOWER feature 


System aimed to recognize between different types of flowers, and between bud, maturity and exhaustion 

swine recognition & inventory

System that allows to monitor shapes and sizes in swine cattle and helps with inventory

Machine learning

We use different techniques and frameworks within ML to tackle challenges that involve predicting results from variable or complex scenarios.



We use what's best and cutting edge in image technology and we work with the best graphic processing platforms like Nvidia, GoPro, Intel.

IoT (internet of things)

We have experience with IoT technology, embedded software, communicating between devices and integrating with cloud systems. 


We can develop our own hardware or combine different third-party devices for rough environment conditions like the farm type


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