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.
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.
