Powering computer vision in Precision Agriculture

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

precision agriculture main img

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

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

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

Yeshiaya Odysight
Yishaia Zabary
Senior Data Scientist
david lempert
David Lempert
Head of R&D
Ido ariav
Ido Ariav
Deep Learning Lead