Orchard Robotics Leverages Scale Rapid for Precision Crop Management
Technology Category
- Analytics & Modeling - Computer Vision Software
- Analytics & Modeling - Machine Learning
Applicable Industries
- Agriculture
- Education
Applicable Functions
- Quality Assurance
Use Cases
- Autonomous Robots
- Visual Quality Detection
Services
- Data Science Services
- Testing & Certification
About The Customer
Orchard Robotics is a company that provides farmers with an AI-first approach to precision crop management. They have developed tractor-mounted, AI-powered camera systems that collect precision data about every tree in an orchard. Their Orchard OS software platform integrates with existing farm operations, allowing farmers to act on this data directly. By using machine learning models to extract insights from terabytes of image data, Orchard Robotics enables farmers to manage their crops precisely, producing more food for the world, much more efficiently.
The Challenge
Orchard Robotics, a company providing AI-first precision crop management solutions to farmers, faced a significant challenge in collecting and utilizing precision data across vast commercial orchards. The company developed tractor-mounted, AI-powered camera systems to collect precision data about every tree. However, the company needed to accurately count every fruit on every tree, a task that proved to be incredibly difficult and tedious, especially when the fruit was small. As a small team, Orchard Robotics struggled to scale these annotations in-house. They initially tried using three other major data-labeling services, but they could not achieve the consistent quality they needed. The quality varied dramatically between batches, and they could not provide feedback to the annotators on the quality of the labels. These platforms also did not offer ellipses as an annotation type, forcing Orchard Robotics to rely on bounding boxes, a less-than-ideal option when labeling spherical fruit.
The Solution
Orchard Robotics turned to Scale Rapid for high-quality annotations with attention to detail. Scale Rapid provided results for their annotation batches in as little as 12 hours, a significant reduction from the 4-5 days it took with previous services. The quality of their annotations also improved, with Scale Rapid returning high-quality annotations, even for complex, high-resolution images. Scale Rapid allowed Orchard Robotics to provide direct feedback to annotators and monitor annotation progress, ensuring reliable, high-quality annotations within a clearly defined timeline. Scale Rapid also automatically controlled the quality of the labels and provided an API that allowed Orchard Robotics to automate their annotation requests as they continued to grow.
Operational Impact
Quantitative Benefit
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