Powering computer vision in Precision Agriculture

Weaving human and machine intelligence to help deep learning teams reach production at scale

Visual AI And Data Processing For precision-agriculture

Enriching AI capabilities in
precision agriculture

Computer vision models can improve a wide range of farming applications – from monitoring crops and produce to livestock and aquaculture. Developing such applications, however, means working with unstructured, unpredictable, and highly dynamic environments, in which terrain and targeted objects vary and change constantly. Preparing agricultural data also requires expertise, both during analysis and implementation.

To answer such demands, Dataloop’s platform integrates automatic active learning and data processing functions, combined with expert human-in-the-loop workforce for data validation. This means you can use your models to pre-annotate and pre-process data that comes directly from your production environment, then have expert annotators audit and fix only edge cases – resulting in continuously improved model accuracy and significant saving of resources and time, even in highly dynamic and diverse agricultural settings.

Dataloop's real-world applications in agtech
Select use cases that Dataloop advances

Growth Monitoring
Growth monitoring
Plant population
Crop monitoring
Insect and disease treatment
Pest & disease diagnosis
Produce health
Crop harvesting
Weed detection
Weed control
Livestock monitoring
Livestock monitoring

Dataloop’s Use-cases

Enterprise-grade data engine

Ensuring highest data standards that serve your entire data organization, allowing cross-functional collaboration while keeping your data access internal

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Quality

High performance for large volumes of data, 99.9% uptime SLA, providing auto-scaling infrastructure that supports your ever-growing data operations

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Security

Full encryption keeping your data constantly safe, compliant with military-grade security standards and certified by comprehensive system audits

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Privacy

Custom authentication and role permission enforcement. Using advanced Kubernetes-powered infrastructure for on-premises or private storage deployments

Powering vision AI teams across the globe
Helping companies create AI-driven products, from development to large-scale production

Michael Maman
CTO, Seedo
Ido Ariav
Deep Learning Lead,
Elbit Systems
Nikita Chizhov
SW Developer for AI Pipeline, Nyris GmbH