Taranis needed to upload large volumes of high-resolution drone images from remote locations worldwide and scale infrastructure to train complex machine learning models. Each drone flight collects 10,000 images of 10-20MB each. Up to 40% of crops are routinely lost due to insects, disease, weeds, and nutrient deficiencies. The company needed better connectivity, speed, and scalable processing power without massive infrastructure investment.
Taranis migrated to Google Cloud Platform, leveraging global data centers for fast connectivity and V100 GPUs on Compute Engine for image processing. The solution includes automatic scaling from 1,000 to 4,000 V100s, Kubernetes Engine for satellite image processing, Cloud SQL for data storage, and TensorFlow for machine learning model training. The platform processes 100 million distinct features across 700,000 images.
Agriculture is a seasonal business, so we have certain months of peak activity followed by quiet months, and we also have peaks throughout the day. During quiet times, we can scale back all our high-level Compute Engine GPU resources automatically so we don't have to prepare our system in advance.
Eli Bukchin, Co-founder and CTO
According to a United Nations report, the world's population will reach 9.8 billion by 2050, requiring significant increases in food production. Meanwhile, urbanization and unpredictable weather patterns are reducing agricultural output. Taranis addresses this challenge using drone technology and AI to help farmers reduce crop loss, increase yields, and lower costs. Founded in 2014, the company now manages over 20 million acres worldwide through its intelligence platform.
Taranis collects vast amounts of data from remote locations including Russia, Eastern Europe, and South America. Each drone flight captures around 10,000 images, with each image between 10-20MB. The company needed to upload these large volumes quickly while maintaining the processing power to train complex machine learning models. Up to 40% of crops are routinely lost due to insects, disease, weeds, and nutrient deficiencies, making early detection critical.
Taranis migrated to Google Cloud to leverage global data centers offering fast connectivity for their 30TB throughput. The solution uses V100 GPUs on Compute Engine with automatic scaling from 1,000 to 4,000 units based on demand. The architecture includes Kubernetes Engine for satellite image processing, Cloud SQL for data storage, Cloud Functions, and Cloud Pub/Sub. This flexibility allows the company to scale back resources during quiet agricultural seasons automatically.
Taranis uses TensorFlow for machine learning model training, processing tens of millions of photographs collected over the past year and a half. Each photo contains up to a thousand items of interest, such as insect damage or leaf discoloration. In total, the company has processed around 100 million distinct features across 700,000 images. The open source TensorFlow community provides extensive support for rapid model development.
The migration to Google Cloud reduced upload times from a full day to just a few hours - three to four times faster than before. The company now releases features almost continuously using Kubernetes parallel deployments, reducing downtime and eliminating scheduled updates. This allows faster product improvement and quicker feedback cycles. Most significantly, the cost per photo taken is now ten times lower than before.
Taranis is exploring additional Google Cloud tools including Cloud Bigtable, BigQuery, and Cloud Dataflow for better data analytics and business intelligence insights. The company plans further geographical expansion and continuous improvement of machine learning models for detecting new disease categories. Thanks to the scalable infrastructure, the sales team can onboard new customers without worrying about capacity constraints.
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