Pythian helps Semios scale its data analytics for produce growers through a Google Cloud Migration

Customer Success

Pythian helps Semios scale data analytics through Google Cloud Migration

Semios provides precision agriculture technology to help tree fruit and nut farmers worry less about their crops, but its original, on-premise data center struggled to scale. The company needed a robust cloud solution that could scale easily and combine data from several different sources. Semios engaged Pythian experts to help migrate its on-premise data ingestion, storage, and processing to Google Cloud. 

Overview

Semios
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Semios provides a real-time platform that uses networked sensors to track and optimize responses to plant health, pests, and possible crop disease. It is a valuable tool for product growers in the fight to improve crop yields. 

Semios’ original, on-premise data center wasn’t very flexible, however, and as new clients and more data were brought on board, it struggled to scale up. The company needed a robust cloud solution that could scale easily, with more reliable sensor data capture and data integration tools to seamlessly and quickly combine sensor data with several of their vital data points such as satellite imagery and geospatial datasets. It also needed machine learning tools to help predict when problems might arise. And it all had to be delivered, in real-time, to mobile devices so farmers could identify and act on issues in the field, as they happened. 

Semios turned to Pythian to help with the migration to Google Cloud.

What we did

  • Helped Semios migrate their on-premise data ingestion, storage, and processing to Google Cloud, combining IoT sensor data with other information from Google Earth Engine and implementing machine learning models through TensorFlow

Technologies used

  • Google Cloud
  • TensorFlow
  • Cloud Functions
  • Cloud Machine Learning Engine 
  • Google Earth Engine 

Key Outcomes

Semios now offers an intuitive dashboard providing real-time metrics to inform decision-making around conditions and what actions to take to improve product quality. 


Reduced operating costs with a cloud-based solution that processes more than 166 million data points every day and helps growers make decisions based on real-time data 

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